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In the framework of wavelet-based analysis, this paper deals with texture modeling for classification or retrieval systems using non-Gaussian multivariate statistical features. We propose a stochastic model based on Spherically Invariant Random Vectors (SIRVs) joint density function with Weibull assumption to characterize the dependences between wavelet coefficients. For measuring similarity between...
This paper deals with texture analysis based on multiscale stochastic modeling. In contrast to common approaches using symmetric marginal probability density functions of subband coefficients, experimental manipulations show that the symmetric shape assumption is violated for several texture classes. From this fact, we propose in this paper to exploit this shape property to improve texture characterization...
In this paper a relevance feedback (RF) approach for content based image retrieval (CBIR) is described and evaluated. The approach uses Gaussian mixture (GM) models of the image features and a query that is updated in a probabilistic manner. This update reflects the preferences of the user and is based on the models of both positive and negative feedback images. Retrieval is based on a recently proposed...
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