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Greedy Mean Squared Residue for Texture Images Retrieval

  • Salah BouguerouaEmail author
  • Bachir Boucheham
Conference paper
  • 511 Downloads
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 1)

Abstract

In this paper, we propose a new algorithm for texture retrieval, using clustering strategy. Indeed, it is largely noticed that in existing CBIR systems and methods, the collection of the images similar to the query is realized on the basis of comparison of the database images to the query solely. Hence, the results might not be globally homogeneous. In this paper, the collection of the images most similar to the query is realized considering the global homogeneity of the whole cluster (result). Knowing that this is of an exponential order problem, we use a greedy solution consisting in growing the cluster corresponding to a query, one image at a time, based on the Mean Squared residue measure of Cheng and Church (Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology, 2000) [1], originally proposed for the biclustering of gene expression data. At each stage, the new added image to the cluster will be that that preserves most the homogeneity of the current cluster. The texture descriptor used in this work is the uniform-LBP. Experimentations were conducted on two texture image databases, Outext and Brodatz. The proposed algorithm shows an interesting performance compared to the uniform-LBP combined to Euclidean metric.

Keywords

CBIR Image retrieval Biclustering Mean squared residue Texture Similarity measure Greedy search Optimization 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Computer Science20 Août 1955 University of SkikdaSkikdaAlgeria

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