Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Machine learning, neural and statistical classification
A fast fixed-point algorithm for independent component analysis
Neural Computation
Visual information retrieval
Classification of scene photographs from local orientations features
Pattern Recognition Letters - Selected papers from the 11th scandinavian conference on image analysis
Indoor-Outdoor Image Classification
CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
PicHunter: Bayesian Relevance Feedback for Image Retrieval
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Empirical Evaluation of Dissimilarity Measures for Color and Texture
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Global semantic classification of scenes using power spectrum templates
IM'99 Proceedings of the 1999 international conference on Challenge of Image Retrieval
Image classification for content-based indexing
IEEE Transactions on Image Processing
Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance
IEEE Transactions on Image Processing
Image Retrieval: Color and Texture Combining Based on Query-Image
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
Content-based image retrieval methods
Programming and Computing Software
Image Classification Approach Based on Manifold Learning in Web Image Mining
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Nonparametric estimation of fisher vectors to aggregate image descriptors
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
Effectiveness of ICF features for collection-specific CBIR
AMR'11 Proceedings of the 9th international conference on Adaptive Multimedia Retrieval: large-scale multimedia retrieval and evaluation
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In this study, independent component analysis (ICA) is used to compute features extracted from natural images. The use of ICA is justified in the context of classification of natural images for two reasons. On the one hand the model of image suggests that the underlying statistical principles may be the same as those that determine the structure of the visual cortex. As a consequence, the filters that ICA produces are adapted to the statistics of natural images. On the other hand, we adopt a non-parametric approach that require density estimation in many dimensions, and independence between features appears as a solution to overthrow the "curse of dimensionality". Hence we introduce several signatures of natural images that use these feature, and we define some similarity measures that correspond to these signatures. These signatures appear as more and more accurate estimations of densities, and the associated distances as estimations of the Kullback-Leibler divergence between the densities. Efficiency of the couple signature/distance is estimated by a K-nearest-neighbour classifier, with a "leave-one-out" procedure for all the signatures we define, and a "bootstrap" based one for the best results.