Content-based image retrieval approach using color and texture applied to two databases (Coil-100 and wang)

Abstract

Content-Based Image Retrieval (CBIR) allows to automatically extracting target images according to objective visual contents of the image itself. Representation of visual features and similarity match are important issues in CBIR. Color, texture and shape information have been the primitive image descriptors in content-based image retrieval systems. This paper presents an efficient image indexing and search system based on color and texture features. The color features are represented by combines 2-D histogram and statistical moments and texture features are represented by a gray level co-occurrence matrix (GLCM). To assess and validate our results, many experiments were held in two color spaces HSV and RGB. The descriptor was implemented to two different databases Coil-100 and Wang. 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, and efficient in terms of results and memory.