Authors:
Mohamed Hamroun
1
;
Sonia Lajmi
2
;
Henri Nicolas
3
and
Ikram Amous
4
Affiliations:
1
Bordeaux University, LABRI Laboratory, Sfax University and MIRACL Laboratory, France
;
2
Sfax University, MIRACL Laboratory and Al Baha University, Tunisia
;
3
Bordeaux University and LABRI Laboratory, France
;
4
Sfax University and MIRACL Laboratory, Tunisia
Keyword(s):
CBIR, Genetic Algorithm GA, Retrieval Image.
Related
Ontology
Subjects/Areas/Topics:
Data Engineering
;
Databases and Data Security
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Human-Computer Interaction
;
Large Scale Databases
;
Multimedia Systems
Abstract:
CBIR (Content-Based Image Retrieval) is an image retrieval method that exploits the feature vector of the image as the retrieval index, which is based upon the content, including colors, textures, shapes and distributions of objects in the image, etc. The implementation of the image feature vector and the searching process take a great influence upon the efficiency and result of the CBIR. In this paper, we are introducing a new CBIR system called ISE based on the optimum combination of color and texture descriptors, in order to improve the quality of image recovery using the Particle Swarm Optimization algorithm (PSO). Our system operates also the Interactive Genetic Approach (GA) for a better research output. The performance analysis
shows that the suggested 'DC' method upgrades the average precision metric from 66.6% to 89.50% for the Food category color histogram, from 77.7% to 100% concerning CCV for the Flower category, and from 44.4% to 67.65% regarding co-occurrence matrix for
the Building category using the Corel data set. Besides, our ISE system showcases an average precision of% 95.43 which is significantly higher than other CBIR systems presented in related works.
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