Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons
International Journal of Computer Vision
Weakly Supervised Learning of Visual Models and Its Application to Content-Based Retrieval
International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
A Sparse Texture Representation Using Local Affine Regions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal Random Matchings on Trees and Applications
APPROX '08 / RANDOM '08 Proceedings of the 11th international workshop, APPROX 2008, and 12th international workshop, RANDOM 2008 on Approximation, Randomization and Combinatorial Optimization: Algorithms and Techniques
Local radon transform and earth mover's distances for content-based image retrieval
MMM'08 Proceedings of the 14th international conference on Advances in multimedia modeling
Object-based image retrieval using the statistical structure of images
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Multimedia news exploration and retrieval by integrating keywords, relations and visual features
Multimedia Tools and Applications
Comparative study of global color and texture descriptors for web image retrieval
Journal of Visual Communication and Image Representation
Texture databases - A comprehensive survey
Pattern Recognition Letters
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Image segmentation is not only hard and unnecessary for texture-based image retrieval, but can even be harmful. Images of either individual or multiple textures are best described by distributions of spatial frequency descriptors, rather than single descriptor vectors over pre-segmented regions.A retrieval method based on the Earth Movers Distance with an appropriate ground distance is shown to handle both complete and partial multi-textured queries. As an illustration, different images of the same type of animal are easily retrieved together. At the same time, animals with subtly different coats, like cheetahs and leopards, are properly distinguished.