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Selecting a Discriminant Subset of Co-occurrence Matrix Features for Texture-Based Image Retrieval
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Selecting a Discriminant Subset of Co-occurrence Matrix Features for Texture-Based Image Retrieval
Najlae Idrissi1, 2 , José Martinez1 and Driss Aboutajdine2 
| (1) |
Atlas-GRIM, INRIA & LINA (FRE CNRS 2729) Polytechnic School of the University of Nantes BP 50609, 44306 Nantes cedex 03, France |
| (2) |
GSCM Science Faculty of Rabat, University Mohamed V Rabat, Morocco |
Abstract
In the general case, searching for images in a content-based image retrieval (CBIR) system amounts essentially, and unfortunately,
to a sequential scan of the whole database. In order to accelerate this process, we want to generate summaries of the image
database. In this paper, we focus on the selection of the texture features that will be used as a signature in our forthcoming
system. We analysed the descriptors extracted from grey-level co-occurrence matrices’s (COM) under the constraints imposed
by database systems.
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