Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structure driven image database retrieval
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Spatial Color Indexing and Applications
International Journal of Computer Vision
What Is the Role of Independence for Visual Recognition?
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
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
Feature selection by maximum marginal diversity: optimality and implications for visual recognition
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Image classification for content-based indexing
IEEE Transactions on Image Processing
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
A database centric view of semantic image annotation and retrieval
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Supervised Learning of Semantic Classes for Image Annotation and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integrated patch model: A generative model for image categorization based on feature selection
Pattern Recognition Letters
Features for image retrieval: an experimental comparison
Information Retrieval
Distinctive and compact features
Image and Vision Computing
Learning to classify by ongoing feature selection
Image and Vision Computing
Integrating hierarchical feature selection and classifier training for multi-label image annotation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Conditional infomax learning: an integrated framework for feature extraction and fusion
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Feature selection for image categorization
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Salient feature selection for visual concept learning
PCM'05 Proceedings of the 6th Pacific-Rim conference on Advances in Multimedia Information Processing - Volume Part I
Information-theoretic selection of high-dimensional spectral features for structural recognition
Computer Vision and Image Understanding
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Problems such as object recognition or image retrieval require feature selection (FS) algorithms that scale well enough to be applicable to databases containing large numbers of image classes and large amounts of data per class. We exploit recent connections between information theoretic feature selection and minimum Bayes error solutions to derive FS algorithms that are optimal in a discriminant sense without compromising scalability. We start by formalizing the intuition that optimal FS must favor discriminant features while penalizing discriminant features that are redundant. We then rely on this result to derive a new family of FS algorithms that enables an explicit trade-off between complexity and classification optimality. This trade-off is controlled by a parameter that encodes the order of feature redundancies that must be explicitly modeled to achieve the optimal solution. Experimental results on databases of natural images show that this order is usually low, enabling optimal FS with very low complexity.