Elements of information theory
Elements of information theory
Saliency, Scale and Image Description
International Journal of Computer Vision
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
A Sparse Texture Representation Using Local Affine Regions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Features for Object Class Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
Multi-dimensional Scale Saliency Feature Extraction Based on Entropic Graphs
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
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A new approach for multi-dimensional Scale Saliency (MDSS) was lately introduced. In this approach, the Scale Saliency algorithm by Kadir and Brady is extended to the multi-dimensional domain. The MDSS algorithm is based on alternative entropy and divergence estimation methods whose complexity does not increase exponentially with data dimensionality. However, MDSS has not been applied to any practical problem yet. In this paper we apply the MDSS algorithm to the texture categorization problem, and we provide further experiments in order to assess the suitability of different estimators to the algorithm. We also propose a new divergence measure based on the k-d partition algorithm.