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This paper proposes an innovative spectral feature extraction (SFE) method called prototype space (PS) feature extraction (PSFE) based only on class spectra. The main novelties of the proposed SFE lie in the following: representing channels in a new space called PS, where they are characterized in terms of reflection properties of classes; and proposing uncertainty, angle, and distance measures to distinguish highly correlated and informative channels. Having clustered the channels by Fuzzy C-Means (FCM) in PS, highly correlated and isolated channels are separated by an uncertainty measure. Consequently, PSFE is built by a linear combination of spectra weighted by class membership values of channels that fall in each cluster. Furthermore, we will enrich PSFE with informative channels which are outlier channels identified through their angle and distance with respect to diagonal and cluster centers in PS. In contrast to the previous SFE methods, PSFE substitutes the search strategies with FCM clustering to find relevant channels. Moreover, instead of optimizing separability criteria, the accuracy of classification over a subset of training data is used to decide which disjoint optical region yields maximum accuracy. According to how class spectra are obtained, PSFE incorporates four approaches: knowledge based, supervised, semisupervised, and unsupervised. The latter three PSFE approaches are assessed in two main cases including with and without informative channels and compared with the conventional feature extraction methods. Experimental results demonstrated higher overall accuracy of PSFE compared to its conventional counterparts with limited sample sizes. Index Terms--Feature extraction (FE), hyperspectral images, remote-sensing image classification, spectral feature.

We propose an eigenvector-based heteroscedastic linear dimension reduction (LDR) technique for multiclass data. The technique is based on a heteroscedastic two-class technique which utilizes the so-called Chernoff criterion, and successfully extends the well-known linear discriminant analysis (LDA). The latter, which is based on the Fisher criterion, is incapable of dealing with heteroscedastic data in a proper way. For the two-class case, the between-class scatter is generalized so to capture differences in (co)variances. It is shown that the classical notion of between-class scatter can be associated with Euclidean distances between class means. From this viewpoint, the between-class scatter is generalized by employing the Chernoff distance measure, leading to our proposed heteroscedastic measure. Finally, using the results from the two-class case, a multiclass extension of the Chernoff criterion is proposed. This criterion combines separation information present in the class mean as well as the class covariance matrices. Extensive experiments and a comparison with similar dimension reduction techniques are presented. Index Terms--Linear dimension reduction, linear discriminant analysis, Fisher criterion, Chernoff distance, Chernoff criterion.

We develop a common theoretical framework for combining classifiers which use distinct pattern representations and show that many existing schemes can be considered as special cases of compound classification where all the pattern representations are used jointly to make a decision. An experimental comparison of various classifier combination schemes demonstrates that the combination rule developed under the most restrictive assumptions - the sum rule - outperforms other classifier combinations schemes. A sensitivity analysis of the various schemes to estimation errors is carried out to show that this finding can be justified theoretically. Index Terms - Classification, classifier combination, error sensitivity.

The intrinsic dimensionality of a data set may be useful for understanding the properties of classifiers applied to it and thereby for the selection of an optimal classifier. In this paper we compare the algorithms for two estimators of the intrinsic dimensionality of a given data set and extend their capabilities. One algorithm is based on the local eigenvalues of the covariance matrix in several small regions in the feature space. The other estimates the intrinsic dimensionality from the distribution of the distances from an arbitrary data vector to a selection of its neighbors. The characteristics of the two estimators are investigated and the results are compared. It is found that both can be applied successfully, but that they might fail in certain cases. The estimators are compared and illustrated using data generated from chromosome banding profiles.

The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques and methods imported from statistical learning theory have been receiving increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. In spite of almost 50 years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved. New and emerging applications, such as data mining, web searching, retrieval of multimedia data, face recognition, and cursive handwriting recognition, require robust and efficient pattern recognition techniques. The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field. Index Terms--Statistical pattern recognition, classification, clustering, feature extraction, feature selection, error estimation, classifier combination, neural networks.

Many computer vision and pattern recognition problems may be posed as the analysis of a set of dissimilarities between objects. For many types of data, these dissimilarities are not euclidean (i.e., they do not represent the distances between points in a euclidean space), and therefore cannot be isometrically embedded in a euclidean space. Examples include shape-dissimilarities, graph distances and mesh geodesic distances. In this paper, we provide a means of embedding such non-euclidean data onto surfaces of constant curvature. We aim to embed the data on a space whose radius of curvature is determined by the dissimilarity data. The space can be either of positive curvature (spherical) or of negative curvature (hyperbolic). We give an efficient method for solving the spherical and hyperbolic embedding problems on symmetric dissimilarity data. Our approach gives the radius of curvature and a method for approximating the objects as points on a hyperspherical manifold without optimisation. For objects which do not reside exactly on the manifold, we develop a optimisation-based procedure for approximate embedding on a hyperspherical manifold. We use the exponential map between the manifold and its local tangent space to solve the optimisation problem locally in the euclidean tangent space. This process is efficient enough to allow us to embed data sets of several thousand objects. We apply our method to a variety of data including time warping functions, shape similarities, graph similarity and gesture similarity data. In each case the embedding maintains the local structure of the data while placing the points in a metric space. [ABSTRACT FROM AUTHOR]

In this article, a novel approach to schizophrenia classification using magnetic resonance images (MRI) is proposed. The presented method is based on dissimilarity-based classification techniques applied to morphological MRIs and diffusion-weighted images (DWI). Instead of working with features directly, pairwise dissimilarities between expert delineated regions of interest (ROIs) are considered as representations based on which learning and classification can be performed. Experiments are carried out on a set of 59 patients and 55 controls and several pairwise dissimilarity measurements are analyzed. We demonstrate that significant improvements can be obtained when combining over different ROIs and different dissimilarity measures. We show that combining ROIs using the dissimilarity-based representation, we achieve higher accuracies. The dissimilarity-based representation outperforms the feature-based representation in all cases. Best results are obtained by combining the two modalities. In summary, our contribution is threefold: (i) We introduce the usage of dissimilarity-based classification to schizophrenia detection and show that dissimilarity-based classification achieves better results than normal features, (ii) We use dissimilarity combination to achieve better accuracies when carefully selected ROIs and dissimilarity measures are considered, and (iii) We show that by combining multiple modalities we can achieve even better results. © 2011 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 21, 179-192, 2011 [ABSTRACT FROM AUTHOR]

Abstract: A conventional way to discriminate between objects represented by dissimilarities is the nearest neighbor method. A more efficient and sometimes a more accurate solution is offered by other dissimilarity-based classifiers. They construct a decision rule based on the entire training set, but they need just a small set of prototypes, the so-called representation set, as a reference for classifying new objects. Such alternative approaches may be especially advantageous for non-Euclidean or even non-metric dissimilarities. The choice of a proper representation set for dissimilarity-based classifiers is not yet fully investigated. It appears that a random selection may work well. In this paper, a number of experiments has been conducted on various metric and non-metric dissimilarity representations and prototype selection methods. Several procedures, like traditional feature selection methods (here effectively searching for prototypes), mode seeking and linear programming are compared to the random selection. In general, we find out that systematic approaches lead to better results than the random selection, especially for a small number of prototypes. Although there is no single winner as it depends on data characteristics, the k-centres works well, in general. For two-class problems, an important observation is that our dissimilarity-based discrimination functions relying on significantly reduced prototype sets (3–10% of the training objects) offer a similar or much better classification accuracy than the best k-NN rule on the entire training set. This may be reached for multi-class data as well, however such problems are more difficult. [Copyright &y& Elsevier]

For the sake of classification, spectra are traditionally represented by points in a high-dimensional feature space, spanned by spectral bands. An alternative approach is to represent spectra by dissimilarities to other spectra. This relational representation enables one to treat spectra as connected entities and to emphasize characteristics such as shape, which are difficult to handle in the traditional approach. Several classification methods for relational representations were developed and found to outperform the nearest-neighbor rule. Existing studies focus only on the performance measured by the classification error. However, for real-time spectral imaging applications, classification speed is of crucial importance. Therefore, in this paper, we focus on the computational aspects of the on-line classification of spectra. We show, that classifiers built in dissimilarity spaces may also be applied significantly faster than the nearest-neighbor rule. [Copyright &y& Elsevier]

In this paper, a classification task on dissimilarity representations is considered. A traditional way to discriminate between objects represented by dissimilarities is the nearest neighbor method. It suffers, however, from a number of limitations, i.e., high computational complexity, a potential loss of accuracy when a small set of prototypes is used and sensitivity to noise. To overcome these shortcomings, we propose to use a normal density-based classifier constructed on the same representation. We show that such a classifier, based on a weighted combination of dissimilarities, can significantly improve the nearest neighbor rule with respect to the recognition accuracy and computational effort. [Copyright &y& Elsevier]

Examines the uniform object generation for optimizing one-class classifiers. Identification of target class, a one class of data in one-class classification from the rest of feature space; Proposal of support vector data description to solve the problem of one-class classification; Presentation of results for artificial data and for real world data.

Abstract: In multiple-instance learning (MIL), an object is represented as a bag consisting of a set of feature vectors called instances. In the training set, the labels of bags are given, while the uncertainty comes from the unknown labels of instances in the bags. In this paper, we study MIL with the assumption that instances are drawn from a mixture distribution of the concept and the non-concept, which leads to a convenient way to solve MIL as a classifier combining problem. It is shown that instances can be classified with any standard supervised classifier by re-weighting the classification posteriors. Given the instance labels, the label of a bag can be obtained as a classifier combining problem. An optimal decision rule is derived that determines the threshold on the fraction of instances in a bag that is assigned to the concept class. We provide estimators for the two parameters in the model. The method is tested on a toy data set and various benchmark data sets, and shown to provide results comparable to state-of-the-art MIL methods. [Copyright &y& Elsevier]

Abstract: Samples can be measured with different precisions and reliabilities in different experiments, or even within the same experiment. These varying levels of measurement noise may deteriorate the performance of a pattern recognition system, if not treated with care. Here we seek to investigate the benefit of incorporating prior knowledge about measurement noise into system construction. We propose a kernel density classifier which integrates such prior knowledge. Instead of using an identical kernel for each sample, we transform the prior knowledge into a distinct kernel for each sample. The integration procedure is straightforward and easy to interpret. In addition, we show how to estimate the diverse measurement noise levels in a real world dataset. Compared to the basic methods, the new kernel density classifier can give a significantly better classification performance. As expected, this improvement is more obvious for small sample size datasets and large number of features. [Copyright &y& Elsevier]

Abstract: A linear, discriminative, supervised technique for reducing feature vectors extracted from image data to a lower-dimensional representation is proposed. It is derived from classical linear discriminant analysis (LDA), extending this technique to cases where there is dependency between the output variables, i.e., the class labels, and not only between the input variables. (The latter can readily be dealt with in standard LDA.) The novel method is useful, for example, in supervised segmentation tasks in which high-dimensional feature vectors describe the local structure of the image. The principal idea is that where standard LDA merely takes into account a single class label for every feature vector, the new technique incorporates class labels of its neighborhood in the analysis as well. In this way, the spatial class label configuration in the vicinity of every feature vector is accounted for, resulting in a technique suitable for, e.g. image data. This extended LDA, that takes spatial label context into account, is derived from a formulation of standard LDA in terms of canonical correlation analysis. The novel technique is called the canonical contextual correlation projection (CCCP). An additional drawback of LDA is that it cannot extract more features than the number of classes minus one. In the two-class case this means that only a reduction to one dimension is possible. Our contextual LDA approach can avoid such extreme deterioration of the classification space and retain more than one dimension. The technique is exemplified on a pixel-based medical image segmentation problem in which it is shown that it may give significant improvement in segmentation accuracy. [Copyright &y& Elsevier]

Usually, objects to be classified are represented by features. In this paper, we discuss an alternative object representation based on dissimilarity values. If such distances separate the classes well, the nearest neighbor method offers a good solution. However, dissimilarities used in practice are usually far from ideal and the performance of the nearest neighbor rule suffers from its sensitivity to noisy examples. We show that other, more global classification techniques are preferable to the nearest neighbor rule, in such cases. For classification purposes, two different ways of using generalized dissimilarity kernels are considered. In the first one, distances are isometrically embedded in a pseudo-Euclidean space and the classification task is performed there. In the second approach, classifiers are built directly on distance kernels. Both approaches are described theoretically and then compared using experiments with different dissimilarity measures and datasets including degraded data simulating the problem of missing values. [ABSTRACT FROM AUTHOR]

Abstract: Hidden Markov models (HMMs) have been successfully applied to a wide range of sequence modeling problems. In the classification context, one of the simplest approaches is to train a single HMM per class. A test sequence is then assigned to the class whose HMM yields the maximum a posterior (MAP) probability. This generative scenario works well when the models are correctly estimated. However, the results can become poor when improper models are employed, due to the lack of prior knowledge, poor estimates, violated assumptions or insufficient training data. To improve the results in these cases we propose to combine the descriptive strengths of HMMs with discriminative classifiers. This is achieved by training feature-based classifiers in an HMM-induced vector space defined by specific components of individual hidden Markov models. We introduce four major ways of building such vector spaces and study which trained combiners are useful in which context. Moreover, we motivate and discuss the merit of our method in comparison to dynamic kernels, in particular, to the Fisher Kernel approach. [Copyright &y& Elsevier]

Summary: Detection of malignancies of the bronchial tree in an early stage, such as carcinoma in situ (CIS), augments the cure rate considerably. It has been shown that the sensitivity of autofluorescence bronchoscopy is better than white light bronchoscopy for the detection of CIS and dysplastic lesions. Autofluorescence bronchoscopy is, however, characterized by a low specificity with a high rate of false positive findings. In the present paper we propose to combine autofluorescence bronchoscopy with optical spectroscopy to improve the specificity of autofluorescence imaging, while maintaining the high sensitivity. Standard autofluorescence bronchoscopy was used to find suspect lesions in the upper bronchial tree, and these lesions were subsequently characterized spectroscopically using a custom made fiberoptic probe. Autofluorescence spectra of the lesions as well as reflectance spectra were measured. We will show in this preliminary report that the addition of either of these spectroscopic techniques decreases the rate of false positives findings, with the best results obtained when both spectroscopic modalities are combined. [Copyright &y& Elsevier]