International Journal of Computer Vision
Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons
International Journal of Computer Vision
CBAIVL '98 Proceedings of the IEEE Workshop on Content - Based Access of Image and Video Libraries
SIGGRAPH '78 Proceedings of the 5th annual conference on Computer graphics and interactive techniques
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Outex - New Framework for Empirical Evaluation of Texture Analysis Algorithms
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Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Statistical Approach to Texture Classification from Single Images
International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Material-specific adaptation of color invariant features
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Coherent phrase model for efficient image near-duplicate retrieval
IEEE Transactions on Multimedia
Image retrieval based on multi-texton histogram
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ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
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SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
Modulating Shape Features by Color Attention for Object Recognition
International Journal of Computer Vision
Color and texture image retrieval using chromaticity histograms and wavelet frames
IEEE Transactions on Multimedia
Content-Based Image Retrieval Using Multiresolution Color and Texture Features
IEEE Transactions on Multimedia
IEEE Transactions on Circuits and Systems for Video Technology
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The aim of this paper is to revisit an old theory of texture perception and update its computational implementation by extending it to colour. With this in mind we try to capture the optimality of perceptual systems. This is achieved in the proposed approach by sharing well-known early stages of the visual processes and extracting low-dimensional features that perfectly encode adequate properties for a large variety of textures without needing further learning stages. We propose several descriptors in a bag-of-words framework that are derived from different quantisation models on to the feature spaces. Our perceptual features are directly given by the shape and colour attributes of image blobs, which are the textons. In this way we avoid learning visual words and directly build the vocabularies on these low-dimensional texton spaces. Main differences between proposed descriptors rely on how co-occurrence of blob attributes is represented in the vocabularies. Our approach overcomes current state-of-art in colour texture description which is proved in several experiments on large texture datasets.