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Abstract: In recent years, the spectral clustering method has gained attentions because of its superior performance. To the best of our knowledge, the existing spectral clustering algorithms cannot incrementally update the clustering results given a small change of the data set. However, the capability of incrementally updating is essential to some applications such as websphere or blogsphere. Unlike the traditional stream data, these applications require incremental algorithms to handle not only insertion/deletion of data points but also similarity changes between existing points. In this paper, we extend the standard spectral clustering to such evolving data, by introducing the incidence vector/matrix to represent two kinds of dynamics in the same framework and by incrementally updating the eigen-system. Our incremental algorithm, initialized by a standard spectral clustering, continuously and efficiently updates the eigenvalue system and generates instant cluster labels, as the data set is evolving. The algorithm is applied to a blog data set. Compared with recomputation of the solution by the standard spectral clustering, it achieves similar accuracy but with much lower computational cost. It can discover not only the stable blog communities but also the evolution of the individual multi-topic blogs. The core technique of incrementally updating the eigenvalue system is a general algorithm and has a wide range of applications—as well as incremental spectral clustering—where dynamic graphs are involved. This demonstrates the wide applicability of our incremental algorithm. [Copyright &y& Elsevier]

The success of bilinear subspace learning heavily depends on reducing correlations among features along rows and columns of the data matrices. In this work, we study the problem of rearranging elements within a matrix in order to maximize these correlations so that information redundancy in matrix data can be more extensively removed by existing bilinear subspace learning algorithms. An efficient iterative algorithm is proposed to tackle this essentially integer programming problem. In each step, the matrix structure is refined with a constrained Earth Mover's Distance procedure that incrementally rearranges matrices to become more similar to their low-rank approximations, which have high correlation among features along rows and columns. In addition, we present two extensions of the algorithm for conducting supervised bilinear subspace learning. Experiments in both unsupervised and supervised bilinear subspace learning demonstrate the effectiveness of our proposed algorithms in improving data compression performance and classification accuracy. [ABSTRACT FROM AUTHOR]

The article discusses various reports published within the issue including one on the introduction of an integrated system for recognizing actions and objects, another on a probabilistic framework for three-dimensional (3D) visual object representation and one on the detection of repetitive patterns within deformed 2D lattices.

In this correspondence, we study the extra-factor estimation problem with the assumption that the training image ensemble is expressed as an nth-order tensor with the nth-dimension characterizing all features for an image and other dimensions for different extra factors, such as illuminations, poses, and identities. To overcome the local minimum issue of conventional algorithms designed for this problem, we present a novel statistical learning framework called mode-kn Factor Analysis for obtaining a closed-form solution to estimating the extra factors of any test image. In the learning stage, for the kth (k ≠ n) dimension of the data tensor, the mode-kn patterns are constructed by concatenating the feature dimension and the kth extra-factor dimension, and then a mode-kn factor analysis model is learnt based on the mode-kn patterns unfolded from the original data tensor. In the inference stage, for a test image, the mode classification of the kth dimension is performed within a probabilistic framework. The advantages of mode-kn factor analysis over conventional tensor analysis algorithms are twofold: 1) a closed-form solution, instead of iterative sub-optimal solution as conventionally, is derived for estimating the extra-factor mode of any test image; and 2) the classification capability is enhanced by interacting with the process of synthesizing data of all other modes in the kth dimension. Experiments on the Pointing'04 and CMU PIE databases for pose and illumination estimation both validate the superiority of the proposed algorithm over conventional algorithms for extra-factor estimation. [ABSTRACT FROM AUTHOR]

We present a supervised learning approach for object-category specific restoration, recognition, and segmentation of images which are blurred using an unknown kernel. The novelty of this work is a multilayer graphical model which unifies the lowlevel vision task of restoration and the high-level vision task of recognition in a cooperative framework. The graphical model is an interconnected two-layer Markov random field. The restoration layer accounts for the compatibility between sharp and blurred images and models the association between adjacent patches in the sharp image. The recognition layer encodes the entity class and its location in the underlying scene. The potentials are represented using nonparametric kernel densities and are learnt from training data. Inference is performed using nonparametric belief propagation. Experiments demonstrate the effectiveness of our model for the restoration and recognition of blurred license plates as well as face images. [ABSTRACT FROM AUTHOR]

Automated analysis of human affective behavior has attracted increasing attention from researchers in psychology, computer science, linguistics, neuroscience, and related disciplines. However, the existing methods typically handle only deliberately displayed and exaggerated expressions of prototypical emotions, despite the tact that deliberate behavior differs in visual appearance, audio profile, and timing from spontaneously occurring behavior. To address this problem, efforts to develop algorithms that can process naturally occurring human affective behavior have recently emerged. Moreover, an increasing number of efforts are reported toward multimodal fusion for human affect analysis, including audiovisual fusion, linguistic and paralinguistic fusion, and multicue visual fusion based on facial expressions, head movements, and body gestures. This paper introduces and surveys these recent advances. We first discuss human emotion perception from a psychological perspective. Next, we examine available approaches for solving the problem of machine understanding of human affective behavior and discuss important issues like the collection and availability of training and test data. We finally outline some of the scientific and engineering challenges to advancing human affect sensing technology. [ABSTRACT FROM AUTHOR]

Precise 3-D head pose estimation plays a significant role in developing human-computer interfaces and practical face recognition systems. This task is challenging due to the particular appearance variations caused by pose changes for a certain subject. In this paper, the pose data space is considered as a union of submanifolds which characterize different subjects, instead of a single continuous manifold as conventionally regarded. A novel manifold embedding algorithm dually supervised by both identity and pose information, called snchronized submanifold embedding (SSE), is proposed for person-independent precise 3-D pose estimation, which means that the testing subject may not appear in the model training stage. First, the submanifold of a certain subject is approximated as a set of simplexes constructed using neighboring samples. Then, these simplexized submanifolds from different subjects are embedded by synchronizing the locally propagated poses within the simplexes and at the same time maximizing the intrasubmanifold variances. Finally, the pose of a new datum is estimated as the propagated pose of the nearest point within the simplex constructed by its nearest neighbors in the dimensionality reduced feature space. The experiments on the 3-D pose estimation database, CHIL data for CLEAR07 evaluation, and the extended application for age estimation on FG-NET aging database, demonstrate the superiority of SSE over conventional regression algorithms as well as unsupervised manifold learning algorithms. [ABSTRACT FROM AUTHOR]

For the problem of image registration, the top few reliable correspondences are often relatively easy to obtain, while the overall matching accuracy may fall drastically as the desired correspondence number increases. In this paper, we present an efficient feature matching algorithm to employ sparse reliable correspondence priors for piloting the feature matching process. First, the feature geometric relationship within individual image is encoded as a spatial graph, and the pairwise feature similarity is expressed as a bipartite similarity graph between two feature sets; then the geometric neighborhood of the pairwise assignment is represented by a categorical product graph, along which the reliable correspondences are propagated; and finally a closed-form solution for feature matching is deduced by ensuring the feature geometric coherency as well as pairwise feature agreements. Furthermore, our algorithm is naturally applicable for incorporating manual correspondence priors for semi-supervised feature matching. Extensive experiments on both toy examples and real-world applications demonstrate the superiority of our algorithm over the state-of-the-art feature matching techniques. [ABSTRACT FROM AUTHOR]

Conventional subspace learning or recent feature extraction methods consider globality as the key criterion to design discriminative algorithms for image classification. We demonstrate in this paper that applying the local manner in sample space, feature space, and learning space via linear sub-space learning can sufficiently boost the discriminating power, as measured by discriminating power coefficient (DPC). The proposed solution achieves good classification accuracy gains and shows computationally efficient. Particularly, we approximate the global nonlinearity through a multimodal localized piecewise subspace learning framework, in which three locality criteria can work individually or jointly for any new subspace learning algorithm design. It turns out that most existing subspace learning methods can be unified in such a common framework embodying either the global or local learning manner. On the other hand, we address the problem of numerical difficulty in the large-size pattern classification case, where many local variations cannot be adequately handled by a single global model. By localizing the modeling, the classification error rate estimation is also localized and thus it appears to be more robust and flexible for the model selection among different model candidates. As a new algorithm design based on the proposed framework, the query-driven locally adaptive (QDLA) mixture-of-experts model for robust face recognition and head pose estimation is presented. Experiments demonstrate the local approach to be effective, robust, and fast for large size, multiclass, and multivariance data sets. [ABSTRACT FROM AUTHOR]

Recent research has demonstrated the success of supervised dimensionality reduction algorithms 2DLDA and 2DMFA, which are based on the image-as-matrix representation, in small sample size cases. To solve the convergence problem in 2DLDA and 2DMFA, we propose in this work two new schemes, called Null Space based 2DLDA (NS2DLDA) and Null Space based 2DMFA (NS2DMFA), and apply them to the challenging multi-view face recognition task. First, we convert each 2-D face image (matrix) into a vector and compute the first projection matrix P1 from the null space of the intra-class scatter matrix, such that the samples from the same class are projected to the same point. Then the data are projected and reconstructed with P1. Finally, we re-organize the reconstructed datum into a matrix and then compute the second projection direction P2, in the form of a Kronecker product of two matrices, by maximizing the inter-class scatter. A proof of algorithmic convergence is provided. The experiments on two benchmark multi-view face databases, the CMU PIE and FERET databases, demonstrate that NS2DLDA outperforms Fisherface, Null Space LDA (NSLDA) and 2DLDA. Additionally, NS2DMFA is also demonstrated to be more accurate than MFA and 2DMFA for face recognition. [ABSTRACT FROM AUTHOR]

Factor analysis (FA) seeks to reveal the relationship between an observed vector variable and a latent variable of reduced dimension. It has been widely used in many applications involving high-dimensional data, such as image representation and face recognition. An intrinsic limitation of FA lies in its potentially poor performance when the data dimension is high, a problem known as curse of dimensionality. Motivated by the fact that images are inherently matrices, we develop, in this brief, an FA model for matrix-variate variables and present an efficient parameter estimation algorithm. Experiments on both toy and real-world image data demonstrate that the proposed matrix-variant FA model is more efficient and accurate than the classical FA approach, especially when the observed variable is high-dimensional and the samples available are limited. [ABSTRACT FROM AUTHOR]

Estimating human age automatically via facial image analysis has lots of potential real-world applications, such as human computer interaction and multimedia communication. However, it is still a challenging problem for the existing computer vision systems to automatically and effectively estimate human ages. The aging process is determined by not only the person's gene, but also many external factors, such as health, having style, living location, and weather conditions. Males and females may' also age differently. The current age estimation performance is still not good enough for practical use and more effort has to be put into this research direction. In this paper, we introduce the age manifold learning scheme for extracting face aging features and design a locally adjusted robust regressor for learning and prediction of human ages. The novel approach improves the age estimation accuracy significantly over all previous methods. The merit of the proposed approaches for image-based age estimation is shown by extensive experiments on a large internal age database and the public available FG-NET database. [ABSTRACT FROM AUTHOR]

Principal Components Analysis (PCA) has traditionally been utilized with data expressed in the form of 1-D vectors, but there exists much data such as gray-level images, video sequences, Gabor-filtered images and so on, that are intrinsically in the form of second or higher order tensors. For representations of image objects in their intrinsic form and order rather than concatenating all the object data into a single vector, we propose in this paper a new optimal object reconstruction criterion with which the information of a high-dimensional tensor is represented as a much lower dimensional tensor computed from projections to multiple concurrent subspaces. In each of these subspaces, correlations with respect to one of the tensor dimensions are reduced, enabling better object reconstruction performance. Concurrent subspaces analysis (CSA) is presented to efficiently learn these subspaces in an iterative manner. In contrast to techniques such as PCA which vectorize tensor data, CSA's direct use of data in tensor form brings an enhanced ability to learn a representative subspace and an increased number of available projection directions. These properties enable CSA to outperform traditional algorithms in the common case of small sample sizes, where CSA can be effective even with only a single sample per class. Extensive experiments on images of faces and digital numbers encoded as second or third order tensors demonstrate that the proposed CSA outperforms PCA-based algorithms in object reconstruction and object recognition. [ABSTRACT FROM AUTHOR]

This paper presents a unified solution to three unsolved problems existing in face verification with subspace learning techniques: selection of verification threshold, automatic determination of subspace dimension, and deducing feature fusing weights. In contrast to previous algorithms which search for the projection matrix directly, our new algorithm investigates a similarity metric matrix (SMM). With a certain verification threshold, this matrix is learned by a semidefinite programming approach, along with the constraints of the kindred pairs with similarity larger than the threshold, and inhomogeneous pairs with similarity smaller than the threshold. Then, the subspace dimension and the feature fusing weights are simultaneously inferred from the singular value decomposition of the derived SMM. In addition, the weighted and tensor extensions are proposed to further improve the algorithmic effectiveness and efficiency, respectively. Essentially, the verification is conducted within an affine subspace in this new algorithm and is, hence, called the affine subspace for verification (ASV). Extensive experiments show that the ASV can achieve encouraging face verification accuracy in comparison to other subspace algorithms, even without the need to explore any parameters. [ABSTRACT FROM AUTHOR]

The authors make a case for human centered computing (HCC), which aims to focus a computer revolution on human needs and abilities. As digitized information and functionality evolve into a digital ecosystem upon which all humans increasingly rely, computing must also grow more reflective of and responsive to the ordinary lives of the diverse individuals who make up the world's population. HCC requires considering cognitive and social systems as well as physical locations and cultural environments. The authors distinguish between HCC and human-computer interaction, user-centered design, human computation, and other fields. They mention contributions from neuroscience, domain knowledge, data mining, sensor fusion, and multimodal action. An IEEE task force on HCC has been formed.

In this paper, a novel hybrid dimension reduction technique for classification is proposed based on the hybrid analysis of principal component analysis (PCA) and linear discriminant analysis (LDA). LDA is known for capturing the most discriminant features of the data in the projected space while PCA is known for preserving the most descriptive ones after projection. Our hybrid technique integrates discriminant and descriptive information and finds a richer set of alternatives beyond LDA and PCA in a 2-D parametric space, which fits a specific classification task and data distribution better. Theoretical study shows that our technique also alleviates the singularity problem of scatter matrix, which is caused by small training set, and increases the effective dimension of the projected subspace. In order to find the hybrid features adaptively and avoid exhaustive parameter searching, we further propose a boosted hybrid analysis method that incorporates a nonlinear boosting process to enhance a set of hybrid classifiers and combine them into a more accurate one. Compared with the other techniques that aim at combining PCA and LDA, our approaches are novel because our method finds alternatives to LDA and PCA in a 2-D parameter space and the boosting process provides enhancement and robust combination of the classifiers. Extensive experiments are conducted on benchmark and real image databases to compare our proposed methods with the state-of-the-art linear and nonlinear discriminant analysis techniques. The results show the superior performance of our hybrid analysis methods. [ABSTRACT FROM AUTHOR]

In this paper, we present the logarithmic total variation (LTV) model for face recognition under varying illumination, including natural lighting conditions, where we rarely know the strength, direction, or number of light sources. The proposed LTV model has the ability to factorize a single face image and obtain the illumination invariant facial structure, which is then used for face recognition. Our model is inspired by the SQI model but has better edge-preserving ability and simpler parameter selection. The merit of this model is that neither does it require any lighting assumption nor does it need any training. The LTV model reaches very high recognition rates in the tests using both Yale and CMU PIE face databases as well as a face database containing 765 subjects under outdoor lighting conditions. [ABSTRACT FROM AUTHOR]

Abstract: For learning-based tasks such as image classification, the feature dimension is usually very high. The learning is afflicted by the curse of dimensionality as the search space grows exponentially with the dimension. Discriminant-EM (DEM) proposed a framework by applying self-supervised learning in a discriminating subspace. This paper extends the linear DEM to a nonlinear kernel algorithm, Kernel DEM (KDEM), and evaluates KDEM extensively on benchmark image databases and synthetic data. Various comparisons with other state-of-the-art learning techniques are investigated for several tasks of image classification. Extensive results show the effectiveness of our approach. [Copyright &y& Elsevier]

Automatic classification is one of the basic tasks required in any pattern recognition and human computer interaction application. In this paper, we discuss training probabilistic classifiers with labeled and unlabeled data. We provide a new analysis that shows under what conditions unlabeled data can be used in learning to improve classification performance. We also show that, if the conditions are violated, using unlabeled data can be detrimental to classification performance. We discuss the implications of this analysis to a specific type of probabilistic classifiers, Bayesian networks, and propose a new structure learning algorithm that can utilize unlabeled data to improve classification. Finally, we show how the resulting algorithms are successfully employed in two applications related to human-Computer interaction and pattern recognition: facial expression recognition and face detection. [ABSTRACT FROM AUTHOR]

We present a user-centric system for visualization and layout for content-based image retrieval. Image features (visual and/or semantic) are used to display retrievals as thumbnails in a 2-D spatial layout or “configuration” which conveys all pair-wise mutual similarities. A graphical optimization technique is used to provide maximally uncluttered and informative layouts. Moreover, a novel subspace feature weighting technique can be used to modify 2-D layouts in a variety of context-dependent ways. An efficient computational technique for subspace weighting and re-estimation leads to a simple user-modeling framework whereby the system can learn to display query results based on layout examples (or relevance feedback) provided by the user. The resulting retrieval, browsing and visualization can adapt to the user's (time-varying) notions of content, context and preferences in style and interactive navigation. Monte Carlo simulations with machine-generated layouts as well as pilot user studies have demonstrated the ability of this framework to model or “mimic” users, by automatically generating layouts according to their preferences. [ABSTRACT FROM AUTHOR]

Cytoplasmic transfer (CT) from a donor to a recipient oocyte has been used clinically to facilitate human pregnancies. Data reported here describe the first characterization of CT coincident with intracytoplasmic sperm injection in the mouse system. Sibling oocytes were used to transfer 2, 4, or 6 pl of ooplasm to a recipient egg along with a sperm head using piezo-actuated injection. Survival and fertilization after CT were comparable to controls at 2 pl and 4 pl, but survival was significantly reduced with 6 pl volumes. Development to the blastocyst stage was also inversely related to CT volume, with some decline beginning with the 4 pl CT group. However, some blastocysts did develop in all of the groups. The results are in contrast with human eggs, which tolerate larger CT volumes. Results indicate that the mouse system can be used to characterize the transfer of exogenous materials concomitant with sperm injection, provided that the CT volume is not excessive. INSET: Another timing gene regulating normal mouse development. [ABSTRACT FROM AUTHOR]

The 90 kDa heat shock protein (Hsp90) cooperates with its co-chaperone Cdc37 to provide obligatory support to numerous protein kinases involved in the regulation of cellular signal transduction pathways. In this report, crystal structures of protein kinases were used to guide the dissection of two kinases [the Src-family tyrosine kinase, Lck, and the heme-regulated eIF2α kinase (HRI)], and the association of Hsp90 and Cdc37 with these constmcts was assessed. Hsp90 interacted with both the N-terminal (NL) and C-terminal (CL) lobes of the kinases' catalytic domains. In contrast, Cdc37 interacted only with the NL. The Hsp90 antagonist molybdate was necessary to stabilize the interactions between isolated subdomains and Hsp90 or Cdc37, but the presence of both lobes of the kinases' catalytic domain generated a stable salt-resistant chaperone-client heterocomplex. The Hsp90 co-chaperones FKBP52 and p23 interacted with the catalytic domain and the NL of Lck, whereas protein phosphatase 5 demonstrated unique modes of kinase binding. Cyp40 was a salt labile component of Hsp90 complexes formed with the full-length, catalytic domains, and N-terminal catalytic lobes of Lck and HRI. Additionally, dissections identify a specific kinase motif that triggers Hsp90's conformational switching to a high-affinity client binding state. Results indicate that the Hsp90 machine acts as a versatile chaperone that recognizes multiple regions of non-native proteins, while Cdc37 binds to a more specific kinase segment, and that concomitant recognition of multiple client segments is communicated to generate or stabilize high-affinity chaperoneclient heterocomplexes. [ABSTRACT FROM AUTHOR]

Infrared spectrometry has correlated excellently with mass spectrometry in detecting the ratio of 13CO2 to 12CO2 in breath samples. The present study aimed to evaluate the accuracy of the 13C-urea breath test (13C-UBT) using a new model of infrared analyzer. A total of 600 patients who were undergoing upper endoscopy without receiving eradication therapy were entered into the study. Culture, histology, and rapid urease test on biopsies from the antrum and corpus of the stomach were used for the determination of Helicobacter pylori infection . Breath samples were collected before and 20 min after drinking 100 mg 13C-urea in 100 mL water. The optimal cutoff value was determined by the receiver operating characteristic curve. Of the 586 patients who were eligible for analysis, 369 were positive for H. pylori infection, 185 were negative for H. pylori infection, and 32 were indeterminate. When the appropriate cutoff value was set at 3.5‰, a sensitivity of 97.8%, a specificity of 96.8% and an accuracy of 97.5% were obtained using the 13C-UBT. The accuracy of the 13C-UBT decreased when CO2 concentration in the breath sample was <2%, as compared with ≥2% (93.6% vs 97.7%), mainly because of a decrease in specificity (81.8% vs 97.7%). There were 2.7% of patients with Δ13CO2 values that fell between 3.0–4.5‰, in whom the risk of error was 47%. The 13C-UBT performed with infrared spectrometry is a highly sensitive, specific, and non-invasive method for the detection of H. pylori infection. The immediate availability of the test result and technical simplicity make it particularly effective in routine clinical practice. [ABSTRACT FROM AUTHOR]

We present computer vision techniques for building dressed human models using images. We develop an algorithm for three-dimensional body reconstruction and texture mapping using contour, stereo, and texture information from several images and deformable superquadrics as the model parts. We demonstrate a novel vision technique for analysis of cloth draping behavior. This technique allows for estimation of cloth model parameters, such as bending properties, but can also be used to estimate the contact points between the body and clothing in the range data of dressed humans. Combined with our body reconstruction algorithm and additional constraints on the articulation model, the detection of the garment-body contact points allows construction of a dressed human model in which even the geometry that was covered by clothing in the available data is reasonably well estimated [ABSTRACT FROM PUBLISHER]

The emerging MPEG-4 standard supports the transmission and composition of facial animation with natural video. The new standard will include a facial animation parameter (FAP) set that is defined based on the study of minimal facial actions and is closely related to muscle actions. The FAP set enables model-based representation of natural or synthetic talking-head sequences and allows intelligible visual reproduction of facial expressions, emotions, and speech pronunciations at the receiver. This paper addresses the data-compression issue of talking heads and presents three methods for bit-rate reduction of FAPs. Compression efficiency is achieved by way of transform coding, principal component analysis, and FAP interpolation. These methods are independent of each other in nature and thus can be applied in combination to lower the bit-rate demand of FAPs, making possible the transmission of multiple talking heads over band-limited channels. The basic methods described here have been adopted into the MPEG-4 Visual Committee Draft and are readily applicable to other articulation data such as body animation parameters. The efficacy of the methods is demonstrated by both subjective and objective results [ABSTRACT FROM PUBLISHER]

Human age, as an important personal trait, can be directly inferred by distinct patterns emerging from the facial appearance. Derived from rapid advances in computer graphics and machine vision, computer-based age synthesis and estimation via faces have become particularly prevalent topics recently because of their explosively emerging real-world applications, such as forensic art, electronic customer relationship management, security control and surveillance monitoring, biometrics, entertainment, and cosmetology. Age synthesis is defined to rerender a face image aesthetically with natural aging and rejuvenating effects on the individual face. Age estimation is defined to label a face image automatically with the exact age (year) or the age group (year range) of the individual face. Because of their particularity and complexity, both problems are attractive yet challenging to computer-based application system designers. Large efforts from both academia and industry have been devoted in the last a few decades. In this paper, we survey the complete state-of-the-art techniques in the face image-based age synthesis and estimation topics. Existing models, popular algorithms, system performances, technical difficulties, popular face aging databases, evaluation protocols, and promising future directions are also provided with systematic discussions. [ABSTRACT FROM PUBLISHER]

This article presents a robust algorithm for automated registration of images related by rigid-body transformations. This algorithm uses a new region-based similarity metric, which enables accurate registration of images of large contrast differences. Region segmentation required by the metric is accomplished using a multiscale segmentation algorithm, and minimization of this metric is done using the Powell direction set method. Experimental results are presented to demonstrate that the algorithm is effective for aligning images from single or multiple imaging modalities without the use of any fiducial markers. © 1997 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 8, 513–518, 1997 [ABSTRACT FROM AUTHOR]

We describe a curvature-based approach for estimating nonrigid motion of moving surfaces. We deal with conformal motion, which can be characterized by stretching of the surface. At each point, this stretching is equal in all directions but different for different points. The stretching function can be defined as additional (with global translation and rotation) motion parameter. We present a new algorithm for local stretching recovery from Gaussian curvature, based on polynomial (linear and quadratic) approximations of the stretching function. It requires point correspondences between time frames but not the complete knowledge of nonrigid transformation. Experiments on simulated and real data are performed to illustrate performance and accuracy of derived algorithms. Noise sensitivity of the algorithm is also evaluated using Gaussian noise on simulated data. [ABSTRACT FROM AUTHOR]

A new continuously tunable optoelectronic oscillator based on shifted dispersion‐induced RF‐fading is proposed and experimentally demonstrated. The design provides wideband continuously all‐optical tunability by incorporating the shifted RF‐fading based on a Mach‐Zehnder structure in an optoelectronic feedback loop. By simply varying the path difference between the two arms of the Mach‐Zehnder configuration via an adjustable variable delay line, the fundamental frequency of the oscillator can achieve extremely high and continuous tuning with a frequency upper bound dependent on the delay properties of the variable optical delay line. [ABSTRACT FROM AUTHOR]

Finding geospatial data has been a big challenge regarding the data size and heterogeneity across various domains. Previous work has explored using machine learning to improve geospatial data search ranking, but it usually relies on training data labelled by subject matter experts, which makes it laborious and costly to apply to scenarios in which data relevancy to a query can change over time. When a user interacts with a search engine, plenteous information is recorded in the log file, which is essentially free, sustainable and up-to-the-minute. In this research, we propose a deep learning-based search ranking framework that can expeditiously update the ranking model through capturing real-time user clickstream data. The contributions of the proposed framework consist of 1) a log parser that can ingest and parse Web logs that record users' behavior in a real-time manner; 2) a set of hypotheses of modelling the relative relevance of data; and 3) a deep learning based ranking model which can be updated dynamically with the increment of user behavior data. Quantitative comparison with a few other machine learning algorithms suggests substantial improvement. • A framework supports real time ranking. • A log processor that can ingest and process from Web logs in a real-time manner. • A set of hypotheses of modelling the relative relevance of data. • A deep learning based ranking algorithm that can be trained incrementally using user behavior data. [ABSTRACT FROM AUTHOR]

Nonnegative matrix factorization (NMF) is a popular technique for finding parts-based, linear representations of nonnegative data. It has been successfully applied in a wide range of applications such as pattern recognition, information retrieval, and computer vision. However, NMF is essentially an unsupervised method and cannot make use of label information. In this paper, we propose a novel semi-supervised matrix decomposition method, called Constrained Nonnegative Matrix Factorization (CNMF), which incorporates the label information as additional constraints. Specifically, we show how explicitly combining label information improves the discriminating power of the resulting matrix decomposition. We explore the proposed CNMF method with two cost function formulations and provide the corresponding update solutions for the optimization problems. Empirical experiments demonstrate the effectiveness of our novel algorithm in comparison to the state-of-the-art approaches through a set of evaluations based on real-world applications. [ABSTRACT FROM PUBLISHER]

Content-based multimedia indexing, retrieval, and processing as well as multimedia databases demand the structuring of the media content (image, audio, video, text, etc.), one significant goal being to associate the identity of the content to the individual segments of the signals. In this paper, we specifically address the problem of speaker clustering, the task of assigning every speech utterance in an audio stream to its speaker. We offer a complete treatment to the idea of partially supervised speaker clustering, which refers to the use of our prior knowledge of speakers in general to assist the unsupervised speaker clustering process. By means of an independent training data set, we encode the prior knowledge at the various stages of the speaker clustering pipeline via 1) learning a speaker-discriminative acoustic feature transformation, 2) learning a universal speaker prior model, and 3) learning a discriminative speaker subspace, or equivalently, a speaker-discriminative distance metric. We study the directional scattering property of the Gaussian mixture model (GMM) mean supervector representation of utterances in the high-dimensional space, and advocate exploiting this property by using the cosine distance metric instead of the euclidean distance metric for speaker clustering in the GMM mean supervector space. We propose to perform discriminant analysis based on the cosine distance metric, which leads to a novel distance metric learning algorithm—linear spherical discriminant analysis (LSDA). We show that the proposed LSDA formulation can be systematically solved within the elegant graph embedding general dimensionality reduction framework. Our speaker clustering experiments on the GALE database clearly indicate that 1) our speaker clustering methods based on the GMM mean supervector representation and vector-based distance metrics outperform traditional speaker clustering methods based on the “bag of acoustic features” representation and statistical model-based distance metrics, 2) our advocated use of the cosine distance metric yields consistent increases in the speaker clustering performance as compared to the commonly used euclidean distance metric, 3) our partially supervised speaker clustering concept and strategies significantly improve the speaker clustering performance over the baselines, and 4) our proposed LSDA algorithm further leads to state-of-the-art speaker clustering performance. [ABSTRACT FROM AUTHOR]

Social media networks contain both content and context-specific information. Most existing methods work with either of the two for the purpose of multimedia mining and retrieval. In reality, both content and context information are rich sources of information for mining, and the full power of mining and processing algorithms can be realized only with the use of a combination of the two. This paper proposes a new algorithm which mines both context and content links in social media networks to discover the underlying latent semantic space. This mapping of the multimedia objects into latent feature vectors enables the use of any off-the-shelf multimedia retrieval algorithms. Compared to the state-of-the-art latent methods in multimedia analysis, this algorithm effectively solves the problem of sparse context links by mining the geometric structure underlying the content links between multimedia objects. Specifically for multimedia annotation, we show that an effective algorithm can be developed to directly construct annotation models by simultaneously leveraging both context and content information based on latent structure between correlated semantic concepts. We conduct experiments on the Flickr data set, which contains user tags linked with images. We illustrate the advantages of our approach over the state-of-the-art multimedia retrieval techniques. [ABSTRACT FROM AUTHOR]

We consider the active learning problem, which aims to select the most representative points. Out of many existing active learning techniques, optimum experimental design (OED) has received considerable attention recently. The typical OED criteria minimize the variance of the parameter estimates or predicted value. However, these methods see only global euclidean structure, while the local manifold structure is ignored. For example, I-optimal design selects those data points such that other data points can be best approximated by linear combinations of all the selected points. In this paper, we propose a novel active learning algorithm which takes into account the local structure of the data space. That is, each data point should be approximated by the linear combination of only its neighbors. Given the local reconstruction coefficients for every data point and the coordinates of the selected points, a transductive learning algorithm called Locally Linear Reconstruction (LLR) is proposed to reconstruct every other point. The most representative points are thus defined as those whose coordinates can be used to best reconstruct the whole data set. The sequential and convex optimization schemes are also introduced to solve the optimization problem. The experimental results have demonstrated the effectiveness of our proposed method. [ABSTRACT FROM PUBLISHER]

Matrix factorization techniques have been frequently applied in information retrieval, computer vision, and pattern recognition. Among them, Nonnegative Matrix Factorization (NMF) has received considerable attention due to its psychological and physiological interpretation of naturally occurring data whose representation may be parts based in the human brain. On the other hand, from the geometric perspective, the data is usually sampled from a low-dimensional manifold embedded in a high-dimensional ambient space. One then hopes to find a compact representation,which uncovers the hidden semantics and simultaneously respects the intrinsic geometric structure. In this paper, we propose a novel algorithm, called Graph Regularized Nonnegative Matrix Factorization (GNMF), for this purpose. In GNMF, an affinity graph is constructed to encode the geometrical information and we seek a matrix factorization, which respects the graph structure. Our empirical study shows encouraging results of the proposed algorithm in comparison to the state-of-the-art algorithms on real-world problems. [ABSTRACT FROM AUTHOR]