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Efficient Use of Texture and Color features in Content Based Image Retrieval (CBIR)

EL ASNAOUI Khalid, CHAWKI Youness, AKSASSE Brahim, OUANAN Mohammed


Content-Based Image Retrieval (CBIR) systems have been developed to support the image retrieval based on image properties, such as shape, color and texture. In this paper, we study the problem of image retrieval within large scale databases. In this context, we propose a new descriptor based on 2-D histogram method with statistical moments by integrating the texture using Gabor filters (GF) and by applying distributed computation to research image. This model reduces the searching time and provides a rapid response. To validate our results, several experiments are successfully conducted on the widely used COIL-100 and Wang databases. The performance is measured in terms of recall and precision, also the obtained performances are compared with several state-of-the-art algorithms
and showed that our algorithm is simple, fast, and efficient in term of results and memory.


CBIR, 2-D histogram, Gabor filters, Feature extraction, Color, Texture.

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