Integrating salient colors with rotational invariant texture features for image representation in retrieval systems

Content based image retrieval (CBIR) systems allow searching for visually similar images in large collections based on their contents. Visual contents are usually represented based on their properties like colors, shapes, and textures. In this paper, we propose to integrate two properties of images for constructing a discriminative and robust representation. Firstly, the input image is transformed into the HSV color space and then quantized into a limited number of representative colors. Secondly, texture features based on uniform patterns of rotated local binary patterns (RLBP) are extracted. The characteristics of color histogram populated from the quantized images and texture features are compared and analyzed for image representation. Consequently, the quantized color histogram and histogram of uniform patterns in RLBP are fused together to form a feature vector. Experimental evaluations with frequently used datasets show that the proposed method yields better results as compared to other state-of-the-art techniques.