Jordi Vitrià

Discriminant ECOC: a heuristic method for application dependent design of error correcting output codes

We present a heuristic method for learning error correcting output codes matrices based on a hierarchical partition of the class space that maximizes a discriminative criterion. To achieve this goal, the optimal codeword separation is sacrificed in favor of a maximum class discrimination in the partitions. The creation of the hierarchical partition set is performed using a binary tree. As a result, a compact matrix with high discrimination power is obtained. Our method is validated using the UCI database and applied to a real problem, the classification of traffic sign images.

Traffic Sign Recognition Using Evolutionary Adaboost Detection and Forest-ECOC Classification

The high variability of sign appearance in uncontrolled environments has made the detection and classification of road signs a challenging problem in computer vision. In this paper, we introduce a novel approach for the detection and classification of traffic signs. Detection is based on a boosted detectors cascade, trained with a novel evolutionary version of Adaboost, which allows the use of large feature spaces. Classification is defined as a multiclass categorization problem. A battery of classifiers is trained to split classes in an Error-Correcting Output Code (ECOC) framework. We propose an ECOC design through a forest of optimal tree structures that are embedded in the ECOC matrix. The novel system offers high performance and better accuracy than the state-of-the-art strategies and is potentially better in terms of noise, affine deformation, partial occlusions, and reduced illumination.

Introducing a weighted non-negative matrix factorization for image classification

Non-negative matrix factorization (NMF) technique has been recently proposed for dimensionality reduction. NMF is capable to produce region or part based representations of objects and images. Also, a direct modification of NMF, the weighted non-negative matrix factorization (WNMF) has also been introduced to improve the NMF capabilities of representing positive local data (as color histograms). A comparison between NMF, WNMF and the well-known principal component analysis (PCA) in the context of image patch classification has been carried out and it is claimed that all these three techniques can be combined in a common and unique classifier. This contribution is an extension of a previous study and we introduce the use of the WNMF as well as a probabilistic approach to compare all the three techniques noticing a great improvement in the final recognition results.

Non-negative Matrix Factorization for Face Recognition

The computer vision problem of face classification under several ambient and unfavorable conditions is considered in this study. Changes in expression, different lighting conditions and occlusions are the relevant factors that are studied in this present contribution. Non-negative Matrix Factorization (NMF) technique is introduced in the context of face classification and a direct comparison with Principal Component Analysis (PCA) is also analyzed. Two leading techniques in face recognition are also considered in this study noticing that NMF is able to improve these techniques when a high dimensional feature space is used. Finally, different distance metrics (L1, L2 and correlation) are evaluated in the feature space defined by NMF in order to determine the best one for this specific problem. Experiments demonstrate that the correlation is the most suitable metric for this problem.

Automatic Detection of Intestinal Juices in Wireless Capsule Video Endoscopy

Wireless capsule video endoscopy is a novel and challenging clinical technique, whose major reported drawback relates to the high amount of time needed for video visualization. In this paper, we propose a method for the rejection of the parts of the video resulting not valid for analysis by means of automatic detection of intestinal juices. We applied Gabor filters for the characterization of the bubble-like shape of intestinal juices in fasting patients. Our method achieves a significant reduction in visualization time, with no relevant loss of valid frames. The proposed approach is easily extensible to other image analysis scenarios where the described pattern of bubbles can be found

Analyzing non-negative matrix factorization for image classification

The Non-negative Matrix Factorization technique (NMF) has been recently proposed for dimensionality reduction. NMF is capable to produce a region- or part-based representation of objects and images. This paper experimentally compares NMF to Principal Component Analysis (PCA) in the context of image patch classification. A first finding is that the two techniques are complementary and that their respective performance is correlated to the with-in class scatter. This paper also analyses different techniques to combine these complementary methods. In the first combination scheme the best technique for each class is chosen and the results are merged. The second combination scheme builds a hierarchy of classifiers where again for each classification task the best technique is chosen. Additionally, incorporation of the classification results of neighboring image patches further improves the overall results.

Gender Recognition in Non Controlled Environments

In most of the automatic face classification applications, images should be captured in natural environments, where partial occlusions or high local changes in the illumination are frequent. For this reason, face classification tasks in uncontrolled environment are still nowadays unsolved problems, given that the loss of information caused by these artifacts can easily mislead any classifier. We present in this paper a system to extract robust face features that can be applied to encode information from any zone of the face and that can be used for different face classification problems. To test this method we include the results obtained in different gender classification experiments, considering controlled and uncontrolled environments and extracting face features from internal and external face zones. The obtained rates show, on the one hand, that we can obtain significant information applying the presented feature extraction scheme and, on the other hand, that the external face zone can contribute useful information for classification purposes

Intestinal Motility Assessment With Video Capsule Endoscopy: Automatic Annotation of Phasic Intestinal Contractions

Intestinal motility assessment with video capsule endoscopy arises as a novel and challenging clinical fieldwork. This technique is based on the analysis of the patterns of intestinal contractions shown in a video provided by an ingestible capsule with a wireless micro-camera. The manual labeling of all the motility events requires large amount of time for offline screening in search of findings with low prevalence, which turns this procedure currently unpractical. In this paper, we propose a machine learning system to automatically detect the phasic intestinal contractions in video capsule endoscopy, driving a useful but not feasible clinical routine into a feasible clinical procedure. Our proposal is based on a sequential design which involves the analysis of textural, color, and blob features together with SVM classifiers. Our approach tackles the reduction of the imbalance rate of data and allows the inclusion of domain knowledge as new stages in the cascade. We present a detailed analysis, both in a quantitative and a qualitative way, by providing several measures of performance and the assessment study of interobserver variability. Our system performs at 70% of sensitivity for individual detection, whilst obtaining equivalent patterns to those of the experts for density of contractions.

On the selection and classification of independent features

This paper is focused on the problems of feature selection and classification when classes are modeled by statistically independent features. We show that, under the assumption of class-conditional independence, the class separability measure of divergence is greatly simplified, becoming a sum of unidimensional divergences, providing a feature selection criterion where no exhaustive search is required. Since the hypothesis of independence is infrequently met in practice, we also provide a framework making use of class-conditional Independent Component Analyzers where this assumption can be held on stronger grounds. Divergence and the Bayes decision scheme are adapted to this class-conditional representation. An algorithm that integrates the proposed representation, feature selection technique, and classifier is presented. Experiments on artificial, benchmark, and real-world data illustrate our technique and evaluate its performance.

Local Color Analysis for Scene Break Detection Applied to TV Commercials Recognition

TV commercials recognition is a need for advertisers in order to check the fulfillment of their contracts with TV stations. In this paper we present an approach to this problem based on compacting a representative frame of each shot by a PCA of its color histogram. We also present a new algorithm for scene break detection based on the analysis of local color variations in consecutive frames of some specific regions of the image.