Direct kernel biased discriminant analysis: a new content-based image retrieval relevance feedback algorithm

In recent years, a variety of relevance feedback (RF) schemes have been developed to improve the performance of content-based image retrieval (CBIR). Given user feedback information, the key to a RF scheme is how to select a subset of image features to construct a suitable dissimilarity measure. Among various RF schemes, biased discriminant analysis (BDA) based RF is one of the most promising. It is based on the observation that all positive samples are alike, while in general each negative sample is negative in its own way. However, to use BDA, the small sample size (SSS) problem is a big challenge, as users tend to give a small number of feedback samples. To explore solutions to this issue, this paper proposes a direct kernel BDA (DKBDA), which is less sensitive to SSS. An incremental DKBDA (IDKBDA) is also developed to speed up the analysis. Experimental results are reported on a real-world image collection to demonstrate that the proposed methods outperform the traditional kernel BDA (KBDA) and the support vector machine (SVM) based RF algorithms

Distance-based discriminant analysis method and its applications

This paper proposes a method of finding a discriminative linear transformation that enhances the data's degree of conformance to the compactness hypothesis and its inverse. The problem formulation relies on inter-observation distances only, which is shown to improve non-parametric and non-linear classifier performance on benchmark and real-world data sets. The proposed approach is suitable for both binary and multiple-category classification problems, and can be applied as a dimensionality reduction technique. In the latter case, the number of necessary discriminative dimensions can be determined exactly. Also considered is a kernel-based extension of the proposed discriminant analysis method which overcomes the linearity assumption of the sought discriminative transformation imposed by the initial formulation. This enhancement allows the proposed method to be applied to non-linear classification problems and has an additional benefit of being able to accommodate indefinite kernel

Facial analysis in video : detection and recognition

Biometric authentication systems automatically identify or verify individuals using physiological (e.g., face, fingerprint, hand geometry, retina scan) or behavioral (e.g., speaking pattern, signature, keystroke dynamics) characteristics. Among these biometrics, facial patterns have the major advantage of being the least intrusive. Automatic face recognition systems thus have great potential in a wide spectrum of application areas. Focusing on facial analysis, this dissertation presents a face detection method and numerous feature extraction methods for face recognition. Concerning face detection, a video-based frontal face detection method has been developed using motion analysis and color information to derive field of interests, and distribution-based distance (DBD) and support vector machine (SVM) for classification. When applied to 92 still images (containing 282 faces), this method achieves 98.2% face detection rate with two false detections, a performance comparable to the state-of-the-art face detection methods; when applied to videQ streams, this method detects faces reliably and efficiently. Regarding face recognition, extensive assessments of face recognition performance in twelve color spaces have been performed, and a color feature extraction method defined by color component images across different color spaces is shown to help improve the baseline performance of the Face Recognition Grand Challenge (FRGC) problems. The experimental results show that some color configurations, such as YV in the YUV color space and YJ in the YIQ color space, help improve face recognition performance. Based on these improved results, a novel feature extraction method implementing genetic algorithms (GAs) and the Fisher linear discriminant (FLD) is designed to derive the optimal discriminating features that lead to an effective image representation for face recognition. This method noticeably improves FRGC ver1.0 Experiment 4 baseline recognition rate from 37% to 73%, and significantly elevates FRGC xxxx Experiment 4 baseline verification rate from 12% to 69%. Finally, four two-dimensional (2D) convolution filters are derived for feature extraction, and a 2D+3D face recognition system implementing both 2D and 3D imaging modalities is designed to address the FRGC problems. This method improves FRGC ver2.0 Experiment 3 baseline performance from 54% to 72%

Detection of human actions from movies based on video content analysis

Projecte realitzat mitjançant programa de mobilitat. TONGJI UNIVERSITY. College of Electronics and Information EngineeringThis thesis introduces a system which detects human actions from movies. It combines and unifies many of the present video recognition tools and methods to build a system that can recognize what is happening in a video

A Content-Based Image Retrieval System for Fish Taxonomy

It is estimated that less than ten percent of the world\u27s species have been discovered and described. The main reason for the slow pace of new species description is that the science of taxonomy, as traditionally practiced, can be very laborious: taxonomists have to manually gather and analyze data from large numbers of specimens and identify the smallest subset of external body characters that uniquely diagnoses the new species as distinct from all its known relatives. The pace of data gathering and analysis can be greatly increased by the information technology. In this paper, we propose a content-based image retrieval system for taxonomic research. The system can identify representative body shape characters of known species based on digitized landmarks and provide statistical clues for assisting taxonomists to identify new species or subspecies. The experiments on a taxonomic problem involving species of suckers in the genera Carpiodes demonstrate promising results

Automatic Music Genre Classification of Audio Signals with Machine Learning Approaches

Musical genre classification is put into context byexplaining about the structures in music and how it is analyzedand perceived by humans. The increase of the music databaseson the personal collection and the Internet has brought a greatdemand for music information retrieval, and especiallyautomatic musical genre classification. In this research wefocused on combining information from the audio signal thandifferent sources. This paper presents a comprehensivemachine learning approach to the problem of automaticmusical genre classification using the audio signal. Theproposed approach uses two feature vectors, Support vectormachine classifier with polynomial kernel function andmachine learning algorithms. More specifically, two featuresets for representing frequency domain, temporal domain,cepstral domain and modulation frequency domain audiofeatures are proposed. Using our proposed features SVM act asstrong base learner in AdaBoost, so its performance of theSVM classifier cannot improve using boosting method. Thefinal genre classification is obtained from the set of individualresults according to a weighting combination late fusionmethod and it outperformed the trained fusion method. Musicgenre classification accuracy of 78% and 81% is reported onthe GTZAN dataset over the ten musical genres and theISMIR2004 genre dataset over the six musical genres,respectively. We observed higher classification accuracies withthe ensembles, than with the individual classifiers andimprovements of the performances on the GTZAN andISMIR2004 genre datasets are three percent on average. Thisensemble approach show that it is possible to improve theclassification accuracy by using different types of domainbased audio features