Integrated Color and Intensity MSF Scheme for Image Retrieval Systems

Abstract

Markov Stationary Features (MSF) based on homogeneous Markov chain model, for content-based image analysis, is getting popularity nowadays. It not only considers the distribution of colors, just as the histogram method does, but also characterizes the spatial co-occurrence of histogram patterns. However, handling a large-scale database of images with a degree of heterogeneity, a simple MSF method is not sufficient to discriminate the images as one requires. In this paper, an Integrated Color and Intensity MSF (ICI-MSF) based on non-homogeneous Markov Chain is proposed to overcome this shortcoming. By incorporating spatial co-occurrence of image intensities with the spatial co-occurrence information of colors and exploiting time inhomogeneous Markov chain concept, it is possible to improve certain aspects of the existing methods. Without compromising effectiveness and robustness, the method proposed in this paper keeps the feature level simplicity. Widely recognized databases namely WANG1000 and Corel10800, are used to evaluate and compare the performance of the proposed algorithm with the existing methods. The experimental results justify the effectiveness of the proposed method.