Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling
International Journal of Computer Vision
Image classification and querying using composite region templates
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Equivalence of Julesz Ensembles and FRAME Models
International Journal of Computer Vision - Special issue on Genomic Signal Processing
Optimal Linear Representations of Images for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integrating Relevance Feedback Techniques for Image Retrieval Using Reinforcement Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Distance Metrics with Contextual Constraints for Image Retrieval
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
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We investigate content-based image retrieval employing a representation of images based on the statistics of their spectral components and a new linear dimension reduction technique. This linear dimension reduction technique is designed to optimize class separation with respect to metrics derived from cross-correlation of spectral histograms. Our approach to retrieval involves a preliminary classification step to index images in a database followed by a class-by-class retrieval step. We carry out several experiments with the Corel database and compare the outcome with several results previously reported in the literature.