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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.

Analysis of the Multi-Dimensional Scale Saliency Algorithm and Its Application to Texture Categorization