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A novel method for content-based retrieval of surveillance video data is presented. The study starts from the realistic assumption that the automatic feature extraction is kept simple, i.e. only segmentation and low-cost filtering operations have been applied. The solution is based on a new and generic dissimilarity measure for discriminating video surveillance scenes. This weighted compound measure can be interactively adapted during a session in order to capture the user's subjectivity. Upon this, a key-frame selection and a content-based retrieval system have been developed and tested on several actual surveillance sequences. Experiments have shown how the proposed method is efficient and robust to segmentation errors. [ABSTRACT FROM AUTHOR]

In this paper, we presents a stream-based speech event classification and segmentation method in meeting recordings. Four speech events are considered: normal speech, laughter, cough and pause between talks. hidden Markov Models (HMMs) are used to model these speech events and a model topology optimization using Bayesian Information Criterion (BIC) is applied. Experimental results have shown that our system can obtain satisfying results. Based on the detected speech events, the recording of the meeting is structured using an XML-based description language and is visualized by a browser. [ABSTRACT FROM AUTHOR]

Low-level video analysis is an important step for further semantic interpretation of the video. This provides information about the camera work, video editing process, shape, texture, color and topology of the objects and the scenes captured by the camera. Here we introduce a framework capable of extracting the information about the shot boundaries and the camera and object motion, based on the analysis of spatiotemporal pixel blocks in a series of video frames. Extracting the motion information and detecting shot boundaries using the same underlying principle is the main contribution of this paper. Besides, this original principle is likely to improve robustness of the abovementioned low-level video analysis as it avoids typical problems of standard frame-based approaches and the camera motion information provides critical help to improve the shot boundary detection performance. The system is evaluated using TRECVID data [1] with promising results. [ABSTRACT FROM AUTHOR]

We present a method of 3D image mosaicing for effective 3D representation of roadside buildings and implement an X3D-based Web service for the 3D image mosaics generated by the proposed method. A more realistic 3D facade model is developed by employing the multiple projection planes using sparsely distributed feature points and the sharp corner detection using perpendicular distances between a vertical plane and its feature points. In addition, the location-based image indexing enables stable providing of the 3D image mosaics in X3D format over the Web, using tile segmentation and direct reference to memory address for the selective retrieval of the image-slits around user's location. [ABSTRACT FROM AUTHOR]

We propose a new stereo matching algorithm through energy-based regularization using color segmentation and visibility constraint. Plane parameters in the entire segments are modeled by robust least square algorithm, which is LMedS method. Then, plane parameter assignment is performed by the cost function penalized for occlusion, iteratively. Finally, disparity regularization which considers the smoothness between the segments and penalizes the occlusion through visibility constraint is performed. For occlusion and disparity estimation, we include the iterative optimization scheme in the energy-based regularization. Experimental results show that the proposed algorithm produces comparable performance to the state-of-the-arts especially in the object boundaries, un-textured regions. [ABSTRACT FROM AUTHOR]

In selective object segmentation, the goal is to extract the entire object of interest without regards to homogeneous regions or object shape. In this paper we present the selective image segmentation problem as a classification problem, and use active learning to train an image feature classifier to identify the object of interest. Since our formulation of this segmentation problem uses human interaction, active learning is used for training to minimize the training effort needed to segment the object. Results using several images with known ground truth are presented to show the efficacy of our approach for segmenting the object of interest in still images. The approach has potential applications in medical image segmentation and content-based image retrieval among others. [ABSTRACT FROM AUTHOR]

We present an algorithm for fast filling of missing regions (holes) in images. Holes may be the result of various causes: manual manipulation e.g. removal of an object from an image, errors in the transmission of an image or video, etc. The hole is filled one pixel at a time by comparing the neighborhood of each pixel to other areas in the image. Similar areas are used as clues for choosing the color of the pixel. The neighborhood and the areas that are compared are square shaped. This symmetric shape allows the hole to be filled in an evenly fashion. However, since square areas inside the hole include some uncolored pixels, we introduce a fast and efficient data structure which allows fast comparison of areas, even with partially missing data. The speed is achieved by using a two phase algorithm: a learning phase which can be done offline and a fast synthesis phase. The data structure uses the fact that colors in an image can be represented by a bounded natural number. The algorithm fills the hole from the boundaries inward, in a spiral form to produce a smooth and coherent result. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive hybrid data broadcast scheme for wireless converged networks. Balanced allocation of broadcast resource between Push and Pull and adaptation to the various changes of user request are keys to the successful operation of a hybrid data broadcast system. The proposed scheme is built based on two key features, BEI (Broadcast Efficiency Index) based adaptation and RHPB (Request History Piggy Back) based user request estimation. BEI is an index defined for each data item, and used to determine whether an item should be serviced through Push or Pull. RHPB is an efficient user request sampling mechanism, which utilizes small number of explicit user requests to assess overall user request change. Simulation study shows that the proposed scheme improves responsiveness to user request and resource efficiency by adapting to the various changes of user request. [ABSTRACT FROM AUTHOR]

It is anticipated that head tracked 3D displays will provide the next generation of display suitable for widespread use. Although there is an extensive range of 3D display types currently available, head tracked displays have the advantage that they present the minimum amount of image information necessary for the perception of 3D. The advantages and disadvantages of the various 3D approaches are considered and a single and a multi-user head tracked display are described. Future work based on the findings of a prototype multi-user display that has been constructed is considered. [ABSTRACT FROM AUTHOR]

In this study, we focus on model reconstruction for 3D objects using range images. We propose a crude range image alignment method to overcome the initial estimation problem of the iterative closest point (ICP) algorithm using edge points of range images. Different from previous edge detection methods, we first obtain a function representation of the range image in spherical coordinates. This representation allows detecting smooth edges on the object surface easily by a zero crossing edge detector. We use ICP on these edges to align patches in a crude manner. Then, we apply ICP to the whole point set and obtain the final alignment. This dual operation is performed extremely fast compared to directly aligning the point sets. We also obtain the edges of the 3D object model while registering it. These edge points may be of use in 3D object recognition and classification. [ABSTRACT FROM AUTHOR]

This paper presents and compares two multiple description schemes for coding of stereoscopic video, which are based on H.264. The SS-MDC scheme exploits spatial scaling of one view. In case of one channel failure, SS-MDC can reconstruct the stereoscopic video with one view low-pass filtered. SS-MDC can achieve low redundancy (less than 10%) for video sequences with lower inter-view correlation. MS-MDC method is based on multi-state coding and is beneficial for video sequences with higher inter-view correlation. The encoder can switch between these two methods depending on the characteristics of video. [ABSTRACT FROM AUTHOR]

Textured motion - generally known as dynamic or temporal texture - analysis, classification, synthesis, segmentation and recognition is popular research areas in several fields such as computer vision, robotics, animation, multimedia databases etc. In the literature, several algorithms are proposed to characterize these textured motions such as stochastic and deterministic algorithms. However, there is no study which compares the performances of these algorithms. In this paper, we carry out a complete comparison study. Also, improvements to deterministic methods are given. [ABSTRACT FROM AUTHOR]

In order to experience true Universal Multimedia Access, people want to access their multimedia content anytime, anywhere, and on any device. Several solutions exist which allow content providers to offer video, audio, and graphics to as many devices as possible by using scalable coding techniques. In addition, content providers also need a scalable presentation format to be able to create a presentation once and distribute it to all possible target devices. This paper introduces a scalable presentation format combining MPEG-21 technology with the User Interface Markup Language. The introduced presentation format is based on assigning types to MPEG-21 Digital Items and can be used to create a presentation once, whereupon several device-specific versions can be extracted. The reuse of resource and presentation information together with the use of a device-independent presentation language are hereby the key parameters in the development of the scalable presentation format. [ABSTRACT FROM AUTHOR]

A new class of wavelet-type frames in signal space that uses (anti)symmetric waveforms is utilized for the development of robust error recovery algorithms for transmitted of rich multimedia content over lossy networks. That algorithms use the redundancy inherent in frame expansions. The construction employs interpolatory filters with rational transfer functions that have linear phase. Experimental results recover images when (as much as) 60% of the expansion coeeficients s are either lost or corrupted. Finally, the frame-based error recovery algorithm is compared with a classical coding approach. [ABSTRACT FROM AUTHOR]

Modified subgradient method has been employed to solve super-resolution restoration problem. The technique uses augmented Lagrangians for nonconvex minimization problems with equality constraints. The subgradient of the constructed dual function is used for a measure. Initial results on comparative studies have shown that the technique is very promising. Keywords: Super-resolution, modified subgradient, image reconstruction. [ABSTRACT FROM AUTHOR]

Computer generated holograms generated by using three different numerical techniques are reconstructed optically by spatial light modulators. Liquid crystal spatial light modulators (SLM) on transmission and on reflection modes with different resolutions were investigated. A good match between numerical simulation and optically reconstructed holograms on both SLMs was observed. The resolution of the optically reconstructed images was comparable to the resolution of the SLMs. [ABSTRACT FROM AUTHOR]

Multi-view video coding (MVC) is currently being standardized by International Standardization Organization (ISO) and International Telecommunication Union (ITU). Although translation-based motion compensation can be applied to the picture prediction between different cameras, a better prediction exists if the camera parameters are known. This paper analyses the rules between pictures taken at the parallel or arc camera arrangements where the object is facing to an arbitrary direction. Based on the derived rules, block-width, block-slant and block-height compensations are proposed for the accurate picture prediction. A fast disparity vector detection algorithm and an efficient disparity vector compression algorithm are also discussed. [ABSTRACT FROM AUTHOR]

We propose a new scalable multi-view video coding (SMVC) method with content-aware bit allocation among multiple views. The video is encoded off-line with a predetermined number of temporal and SNR scalability layers. Content-aware bit allocation among the views is performed during bitstream extraction by adaptive selection of the number of temporal and SNR scalability layers for each group of pictures (GOP) according to motion and spatial activity of that GOP. The effect of bit allocation among the multiple views on the overall video quality has been studied on a number of training sequences by means of both quantitative quality measures as well as qualitative visual tests. The number of temporal and SNR scalability layers selected as a function of motion and spatial activity measures for the actual test sequences are "learned" from these bit allocation vs. video quality studies on the training sequences. SMVC with content-aware bit allocation among views can be used for multi-view video transport over the Internet for interactive 3DTV. Experimental results are provided on stereo video sequences. [ABSTRACT FROM AUTHOR]

We propose a novel strategy to obtain a high spatio-temporal resolution video. To this end, we introduce a dual sensor camera that can capture two video sequences with the same field of view simultaneously. These sequences record high resolution with low frame rate and low resolution with high frame rate. This paper presents an algorithm to synthesize a high spatio-temporal resolution video from these two video sequences by using motion compensation and spectral fusion. We confirm that the proposed method improves the resolution and frame rate of the synthesized video. [ABSTRACT FROM AUTHOR]

We present a solution to the problem of allowing collaborative construction and fruition of annotations on georeferenced information, by combining three Web-enabled applications: a plugin annotating multimedia content, an environment for multimodal interaction, and a WebGIS system. The resulting system is unique in its offering a wealth of possibilities for interacting with geographically based material. [ABSTRACT FROM AUTHOR]

Electronic mail is an important communication method for most computer users. Spam e-mails however consume bandwidth resource, fill-up server storage and are also a waste of time to tackle.The general way to label an e-mail as spam or non-spam is to set up a finite set of discriminative features and use a classifier for the detection. In most cases, the selection of such features is empirically verified. In this paper, two different methods are proposed to select the most discriminative features among a set of reasonably arbitrary features for spam e-mail detection. The selection methods are developed using the Common Vector Approach (CVA) which is actually a subspace-based pattern classifier.Experimental results indicate that the proposed feature selection methods give considerable reduction on the number of features without affecting recognition rates. [ABSTRACT FROM AUTHOR]

In this paper we present a new approach for interacting with visual document collections. We propose to model user preferences related to visual documents in order to recommend relevant content according to the user's profile. We have formulated problem as prediction problem and we propose VC-Aspect our flexible mixture model which handles implicit associations between users and the visual features of images. We have implemented the model within a CBIR system and results showed that such approach reduced greatly the page zero problem especially for small devices such as smart-phones and PDAs. [ABSTRACT FROM AUTHOR]

Morphological covariance, one of the most frequently employed texture analysis tools offered by mathematical morphology, makes use of the sum of pixel values, i.e. "volume" of its input. In this paper, we investigate the potential of alternative measures to volume, and extend the work of Wilkinson (ICPR'02) in order to obtain a new covariance operator, more sensitive to spatial details, namely the spatial covariance. The classification experiments are conducted on the publicly available Outex 14 texture database, where the proposed operator leads not only to higher classification scores than standard covariance, but also to the best results reported so far for this database when combined with an adequate illumination invariance model. Keywords: Morphological covariance, spatial moments, colour texture classification. [ABSTRACT FROM AUTHOR]

This paper proposed a novel algorithm named 2D Gabor Wavelets Window (GWW) method. The GWW scans the image top left to bottom right to extract the local feature vectors (LFVs). A parametric feature vector is derived by downsampling and concatenating these LFVs for face representation and recognition. Compared with the Gabor Wavelets representation of the whole image, the total cost is reduced by maximum of 39% whilst the performance achieved better than the conventional PCA method when experimented on both the ORL and XM2VTSDB databases without any preprocessing. [ABSTRACT FROM AUTHOR]

Several models for the print-and-scan channel are available in the literature. We describe a new channel model specifically tuned to the transmission of two-dimensional bar codes and which is suitable not only for scanners, but also for time/space-variant scenarios including web cameras or those embedded in mobile phones. Our model provides an analytical expression for accurately representing the output of the print-and-capture channel, with the additional advantage of directly estimating its parameters from the available captured image, and thus eliminating the need of painstaking training. A full communication system with a two-dimensional bar code has been implemented to experimentally validate the accuracy of the proposed model and the feasibility of reliable transmissions. These experiments confirm that the results obtained with our method outperform those obtained with existing models. [ABSTRACT FROM AUTHOR]

In this work an active learning PNN was used to recognize instru-mental sounds. LPC and MFCC coefficients with different orders were used as features. The best analysis orders were found by using passive PNNs and these sets were used with active learning PNNs. By realizing some experiments, it was shown that the entire performance was improved by using the active learning algorithm. [ABSTRACT FROM AUTHOR]

An approach for securing video transmission on an end system multicast session is described. Existing solutions use encryption techniques that require the use of a shared key. Although they can achieve efficient encryption/decryption and meet the demands of real-time video, a publicly available service needing only the integrity and non-repudiation of the message is not considered. In this study, we offer such a method using public key cryptography. This method can be used in an end system multicast infrastructure where video originates from one source, but spreads with the help of receiving peers. Two different methods are described and compared: 1) Encryption of the entire packet. 2) Encryption of the unique digest value of the transmitted packet (i.e. digitally signing). The receivers then check the integrity of the received packets using the public key provided by the sender. Also, this way the non-repudiation of the transmitted video is provided. [ABSTRACT FROM AUTHOR]

This study proposes DRM architecture for VOD streaming services in a mobile environment. The proposed system architecture consists of DRM Client Manager, in which core components for client services are independently constructed to be used in a mobile environment, and DRM server, which provides DRM services. DRM Client Manager independently exists in the client to maximize efficiency and processing capacity in such a mobile environment, and consists of user interface, license management and player. DRM server consists of streaming server for VOD streaming, contents server, license server, and packager. The proposed system has an architecture suitable for a mobile environment that is difficult to process in the existing DRM architecture and considers the process of super-distribution through an independent manager in the client. Keyword: DRM(Digital Rights Management), Client Manager, Mobile Environments. [ABSTRACT FROM AUTHOR]

Statistical dependence between classifier scores has been shown to affect the verification accuracy for certain decision fusion rules (e.g.,‘majority', ‘and', ‘or'). In this paper, we investigate what are the best decision fusion rules for various statistical dependences between classifiers and check whether the best accuracy depends on the statistical dependence. This is done by evaluating accuracy of decision fusion rules on three jointly Gaussian scores with various covariances. It is found that the best decision fusion rule for any given statistical dependence is one of the three major rules - ‘majority',‘and', ‘or'. The correlation coefficient between the classifier scores can be used to predict the best decision fusion rule, as well as for evaluation of how well-designed the classifiers are. This can be applied to biometric verification; and it is shown using the NIST 24 fingerprint database and the AR face database that the prediction and evaluation agree. [ABSTRACT FROM AUTHOR]

We present a 3-D Multi-view video delivery system where each user receives only the streams required for rendering their viewpoint. This paper proposes a novel method to alleviate adverse effects of the unavoidable delay between the time a client requests a new stream and the time it becomes available. To this effect, lower bit-rate versions of a set of adjacent views are also streamed to the viewer in addition to the currently required views. This ensures that the viewer has a low quality version of a view ready and decodable when an unpredicted viewpoint change occurs until the high quality stream arrives. Bandwidth implications and PSNR improvements are reported for various low quality streams encoded at different bit-rates. Performance comparisons of the proposed system with respect to transmitting all views using MVC and only two views with no low quality neighbors are presented. [ABSTRACT FROM AUTHOR]

The enabling technologies of increasing PC bus bandwidth, multicore processors, and advanced graphics processors combined with a high-perform-ance multi-image camera system are leading to new ways of considering video. We describe scalable varied-resolution video capture, presenting a novel method of generating multi-resolution dialable-shape panoramas, a line-based calibration method that achieves optimal multi-imager global registration across possibly disjoint views, and a technique for recasting mosaicking homographies for arbitrary planes. Results show synthesis of a 7.5 megapixel (MP) video stream from 22 synchronized uncompressed imagers operating at 30 Hz on a single PC. [ABSTRACT FROM AUTHOR]

A novel approach is presented in order to reject correspondence outliers between frames using the parallax-based rigidity constraint for epipolar geometry estimation. In this approach, the invariance of 3-D relative projective structure of a stationary scene over different views is exploited to eliminate outliers, mostly due to independently moving objects of a typical scene. The proposed approach is compared against a well-known RANSAC-based algorithm by the help of a test-bed. The results showed that the speed-up, gained by utilization of the proposed technique as a preprocessing step before RANSAC-based approach, decreases the execution time of the overall outlier rejection, significantly. Keywords: Outlier removal, Parallax-based rigidity constraint, RANSAC. [ABSTRACT FROM AUTHOR]

We present a surface deformation framework for the problem of 3D shape recovery. A spatially smooth and topologically plausible surface mesh representation is constructed via a surface evolution based technique, starting from an initial model. The initial mesh, representing the bounding surface, is refined or simplified where necessary during surface evolution using a set of local mesh transform operations so as to adapt local properties of the object surface. The final mesh obtained at convergence can adequately represent the complex surface details such as bifurcations, protrusions and large visible concavities. The performance of the proposed framework which is in fact very general and applicable to any kind of raw surface data, is demonstrated on the problem of shape reconstruction from silhouettes. Moreover, since the approach we take for surface deformation is Lagrangian, that can track changes in connectivity and geometry of the deformable mesh during surface evolution, the proposed framework can be used to build efficient time-varying representations of dynamic scenes. [ABSTRACT FROM AUTHOR]

In this paper, we propose a practical method for hardware-accelerated rendering of the depth image-based representation (DIBR) object, which is defined in MPEG-4 Animation Framework eXtension (AFX). The proposed method overcomes the drawbacks of the conventional rendering, i.e. it is slow since it is hardly assisted by graphics hardware and surface lighting is static. Utilizing the new features of modern graphic processing unit (GPU) and programmable shader support, we develop an efficient hardware-accelerated rendering algorithm of depth image-based 3D object. Surface rendering in response of varying illumination is performed inside the vertex shader while adaptive point splatting is performed inside the fragment shader. Experimental results show that the rendering speed increases considerably compared with the software-based rendering and the conventional OpenGL-based rendering method. [ABSTRACT FROM AUTHOR]

Today the application of multimodal biometric systems is a common way to overcome the problems, which come with unimodal systems, such as noisy data, attacks, overlapping of similarities, and non-universality of biometric characteristics. In order to fuse multiple identification sources simultaneously, fusion strategies can be applied on different levels. This paper presents a theoretical concept of a methodology to improve those fusions and strategies independently of their application levels. By extracting and merging certain semantic information and integrating it as additional knowledge (e.g. metadata) into the process the fusion can be potentially improved. Thus, discrepancies and irregularities of one biometric trait can be verified by another one and signal errors can be identified and corrected. Keywords: Biometrics, Security of Multimedia Content, Identification and Authentication. [ABSTRACT FROM AUTHOR]

A new approach of enlarging fused biometric databases is presented. Fusion strategies based upon matching score are applied on active biometrics verification scenarios. Consistent biometric data of two traits are used in test scenarios of handwriting and speaker verification. The fusion strategies are applied on multimodal biometrics of two different user types. The real users represent two biometric traits captured from one person. The virtual users are considered as the combination of two traits captured from two discrete users. These virtual users are implemented for database enlargement. In order to investigate the impact of these virtual users, test scenarios using three different semantics of handwriting and speech are accomplished. The results of fused handwriting and speech of exclusively real users and additional virtual users are compared and discussed. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for extracting meaningful synchronous structures from multi-modal signals. Simultaneous processing of multi-modal data can reveal information that is unavailable when handling the sources separately. However, in natural high-dimensional data, the statistical dependencies between modalities are, most of the time, not obvious. Learning fundamental multi-modal patterns is an alternative to classical statistical methods. Typically, recurrent patterns are shift invariant, thus the learning should try to find the best matching filters. We present a new algorithm for iteratively learning multi-modal generating functions that can be shifted at all positions in the signal. The proposed algorithm is applied to audiovisual sequences and it demonstrates to be able to discover underlying structures in the data. [ABSTRACT FROM AUTHOR]

The arousal dimension of human emotions is assessed from two different physiological sources: peripheral signals and electroencephalographic (EEG) signals from the brain. A complete acquisition protocol is presented to build a physiological emotional database for real participants. Arousal assessment is then formulated as a classification problem, with classes corresponding to 2 or 3 degrees of arousal. The performance of 2 classifiers has been evaluated, on peripheral signals, on EEG's, and on both. Results confirm the possibility of using EEG's to assess the arousal component of emotion, and the interest of multimodal fusion between EEG's and peripheral physiological signals. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of applying powerful statistical pattern classification algorithm based on kernel functions to target tracking on surveillance systems. Rather than directly adapting a recognizer, we develop a localizer directly using the regression form of the Support Vector Machines (SVM). The proposed approach considers to use dynamic model together as feature vectors and makes the hyperplane and the support vectors follow the changes in these features. The performance of the tracker is demonstrated in a sensor network scenario with a constant velocity moving target on a plane for surveillance purpose. [ABSTRACT FROM AUTHOR]

This paper presents an affine invariant shape descriptor which could be applied to both binary and gray-level images. The proposed algorithm uses gradient based features which are extracted along the object boundaries. We use two-dimensional steerable G-Filters [1] to obtain gradient information at different orientations. We aggregate the gradients into a shape signature. The signatures derived from rotated objects are shifted versions of the signatures derived from the original object. The shape descriptor is defined as the Fourier transform of the signature. We also provide a distance definition for the proposed descriptor taking shifted property of the signature into account. The performance of the proposed descriptor is evaluated over a database containing license plate characters. The experiments show that the devised method outperforms other well-known Fourier-based shape descriptors such as centroid distance and boundary curvature. [ABSTRACT FROM AUTHOR]

In this paper, we propose an extraction method of optimal interest points to support shape-based image classification and indexing for image database by applying a dynamic threshold that reflects the characteristics of a shape contour. The threshold is dynamically determined by comparing the contour length ratio of the original shape and the approximated polygon while the algorithm is running. Because our algorithm considers the characteristics of the shape contour, it can minimize the number of interest points. For a shape with n contour points, this algorithm has the time complexity O(n logn). Our experiments show the average optimization ratio up to 0.92. We expect that features of shapes extracted from the proposed method are used for shape-based image classification, indexing, and similarity search. [ABSTRACT FROM AUTHOR]

This paper presents a new algorithm for noise reduction in noisy speech recognition in autocorrelation domain. The autocorrelation domain is an appropriate domain for speech feature extraction due to its pole preserving and noise separation features. Therefore, we have investigated this domain for robust speech recognition. In our proposed algorithm we have tried to suppress the effect of noise before using this domain for feature extraction. This suppression is carried out by noise autocorrelation sequence estimation from the first few frames in each utterance and subtracting it from the autocorrelation sequence of noisy signal. We tested our method on the Aurora 2 noisy isolated-word task and found its performance superior to that of other autocorrelation-based methods applied to this task. Keywords: Robust Speech Recognition, Unbiased Autocorrelation Sequence, Noise estimation, Noisy Speech. [ABSTRACT FROM AUTHOR]

The paper presents definition extensions to MPEG-7 metadata ones, mainly related to audio descriptors, introduced to efficiently describe traditional Greek music data features in order to further enable efficient music retrieval. A number of advanced content-based retrieval scenarios have been defined such as query by music rhythm, query by example and humming based on newly introduced music features, query by traditional Greek music genre, chroma-based query. MPEG-7 DDL extensions and appropriate traditional greek music genre dictionary entries are deemed necessary for accounting for the specificities of the music data under question and for attaining efficiency in music information retrieval. The reported work has been undertaken within the framework of an R&D project targeting an advanced music portal offering a number of content-based music retrieval services, namely POLYMNIA [12]. [ABSTRACT FROM AUTHOR]

In this paper, we propose an area-based decision rule for counting the number of people that pass through a given ROI (Region of Interest). This decision rule divides obtained images into 72 sectors and the size of the person is trained to calculate the mean and variance values for each divided sector. These values are then stored in table form and can be used to count people in the future. We also analyze various movements that people perform in the real world. For instance, during busy hours, people frequently merge and split with each other. Therefore, we propose a system for counting the number of passing people more accurately and a way of discovering the direction of their paths. [ABSTRACT FROM AUTHOR]

The performance of blind source separation (BSS) using independent component analysis (ICA) declines significantly in a reverberant environment. The degradation is mainly caused by the residual crosstalk components derived from the reverberation of the interference signal. A post-processing method is proposed in this paper which uses a approximated Wiener filter using short-time magnitude spectra in the spectral domain. The speech signals have a sparse characteristic in the spectral domain, hence the approximated Wiener filtering can be applied by endowing the difference weights to the other signal components. The results of the experiments show that the proposed method improves the noise reduction ratio(NRR) by about 3dB over conventional FDICA. In addition, the proposed method is compared to the other post-processing algorithm using NLMS algorithm for post-processor [6], and show the better performances of the proposed method. [ABSTRACT FROM AUTHOR]

Event recognition is probably the ultimate purpose of an automated surveillance system. In this paper, hidden Markov models (HMM) are utilized to recognize the nature of an event occurring in a scene. For this purpose, object trajectories, which are obtained through a successful track, are obtained as a sequence of flow vectors that contain instantaneous velocity and location information. These vectors are clustered by K-means algorithm to obtain a prototype representation. HMMs are trained with sequences obtained from usual motion patterns and abnormality is detected by measuring distances to these models. In order to specify the number of models automatically, a novel approach is proposed which utilizes the clues provided by centroid clustering. Preliminary experimental results are promising for detecting abnormal events. [ABSTRACT FROM AUTHOR]

This paper presents paper retrieval using the specific paper features chain and laid lines. Paper features are detected in digitized paper images and they are represented such that they could be used for retrieval. Optimal retrieval performance is achieved by means of a trainable similarity measure for a given set of paper features. By means of these methods a retrieval system is developed that art experts could use real-time in order to speed up their paper research. [ABSTRACT FROM AUTHOR]

In this paper, we study the human action classification problem based on motion features directly extracted from video. In order to implement a fast classification system, we select simple features that can be obtained from non-intensive computation. We also introduce the new SVM_2K classifier that can achieve improved performance over a standard SVM by combining two types of motion feature vector together. After learning, classification can be implemented very quickly because SVM_2K is a linear classifier. Experimental results demonstrate the method to be efficient and may be used in real-time human action classification systems. [ABSTRACT FROM AUTHOR]

In this paper, we propose an efficient image retrieval method that extracts features through partial entropy decoding from JPEG-2000 compressed images. Main idea of the proposed method is to exploit the context information that is generated during context-based arithmetic encoding/decoding with three bit-plane coding passes. In the framework of JPEG-2000, the context of a current coefficient is determined depending on pattern of the significance and/or sign of its neighbors. One of nineteen contexts is at least assigned to each bit of wavelet coefficients starting from MSB (most significant bit) to LSB (least significant bit). As the context contains the directional variation of the corresponding coefficient's neighbors, it represents the local property of image. In the proposed method, the similarity of given two images is measured by the difference between their context histograms in bit-planes. Through simulations, we demonstrate that our method achieves good performance in terms of the retrieval accuracy as well as the computational complexity. [ABSTRACT FROM AUTHOR]

According to the growth of the mobile industry, a lot of on/off-line contents are being converted into mobile contents. Although the cartoon contents especially are one of the most popular mobile contents, it is difficult to provide users with the existing on/off-line contents without any considerations due to the small size of the mobile screen. In existing methods to overcome the problem, the cartoon contents on mobile devices are manually produced by computer software such as Photoshop. In this paper, we automatically produce the cartoon contents fitting for the small screen, and introduce a clustering method useful for variety types of cartoon images as a prerequisite stage for preserving semantic meaning. Texture information which is useful for gray-scale image segmentation gives us a good clue for semantic analysis and self-organizing feature maps (SOFM) is used to cluster similar texture information. Besides we automatically segment the clustered SOFM outputs using agglomerative clustering. In our experimental results, combined approaches show good results of clustering in several cartoons. [ABSTRACT FROM AUTHOR]