Computational models of visual processing
Computational models of visual processing
Pyramid-based texture analysis/synthesis
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Periodicity, Directionality, and Randomness: Wold Features for Image Modeling and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Minimax entropy principle and its application to texture modeling
Neural Computation
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
Image quilting for texture synthesis and transfer
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
FRAME: Filters, Random fields, and Minimax Entropy-- Towards a Unified Theory for Texture Modeling
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Texture Features and Learning Similarity
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Texture Recognition Using a Non-Parametric Multi-Scale Statistical Model
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Texture synthesis for digital painting
SIGGRAPH '84 Proceedings of the 11th annual conference on Computer graphics and interactive techniques
Texture Synthesis by Non-Parametric Sampling
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Application-independent feature selection for texture classification
Pattern Recognition
Towards effective evaluation of geometric texture synthesis algorithms
Proceedings of the Symposium on Non-Photorealistic Animation and Rendering
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Many attempts have been made to characterize latent structures in ''texture spaces'' defined by attentive similarity judgments. While an optimal description of perceptual texture space remains elusive, we suggest that the similarity judgments gained from these procedures provide a useful standard for relating image statistics to high-level similarity. In the present experiment, we ask subjects to group natural textures into visually similar clusters. We also represent each image using the features employed by three different parametric texture synthesis models. Given the cluster labels for our textures, we use linear discriminant analysis to predict cluster membership. We compare each model's assignments to human data for both positive and contrast-negated textures, and evaluate relative model performance.