Work place: Department of Electronics, Djillali Liabes University, Sidi Bel-Abbes, Algeria

E-mail: Kamel.Belloulata@USherbrooke.ca


Research Interests: Image Processing, Image Manipulation, Image Compression, Pattern Recognition, Computer systems and computational processes


Kamel BELLOULATA was born in Algeria in 1969. He received the D.E.A. and Ph.D. degrees in signal, image, and speech processing from the Institut National des Sciences Appliquées (INSA) de Lyon, France, in 1994 and 1998, respectively. From September 1998 to august 1999, he was a Postdoctoral Fellow at INRS-Télécommunications, Montréal, Qc, Canada. From September 1999 to December 2001, he has been an Assistant Professor with the Department of Electrical Engineering of University of Moncton, NB, Canada. Since January 2002, he has been an Associate Professor with the Electrical and Computer Engineering Department of University of Sherbrooke, Qc, Canada. Currently he is Full Professor at the University of Sidi Bel Abbs, Algeria. His current research interests are in the areas of image and video compression, pattern recognition and semantic segmentation.

Author Articles
A New Approach to Region Based Image Retrieval using Shape Adaptive Discrete Wavelet Transform


DOI: https://doi.org/10.5815/ijigsp.2016.01.01, Pub. Date: 8 Jan. 2016

In this paper, we present an efficient region-based image retrieval method, which uses multi-features color, texture and edge descriptors. In contrast to recent image retrieval methods, which use discrete wavelet transform (DWT), we propose using shape adaptive discrete wavelet transform (SA-DWT). The advantage of this method is that the number of coefficients after transformation is identical to the number of pixels in the original region. Since image data is often stored in compressed formats: JPEG 2000, MPEG 4…; constructing image histograms directly in the compressed domain, allows accelerating the retrieval operation time, and reducing computing complexities. Moreover, SA-DWT represents the best way to exploit the coefficients characteristics, and properties such as the correlation. Characterizing image regions without any conversion or modification is first addressed. Using edge descriptor to complement image region characterizing is then introduced. Experimental results show that the proposed method outperforms content based image retrieval methods and recent region based image retrieval methods.

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