Construction of Statistical Visual Descriptors for CBIR Exploiting Time-inhomogeneous Markov Chain Model

Abstract

Markov stationary features (MSF) based on homogeneous Markov chain model for content-based image retrieval (CBIR) is getting popularity nowadays. It not only considers the distribution of colors that the histogram method does, but also characterizes the spatial co-occurrence of histogram patterns. However, handling a large scale database of images with large degree of heterogeneity, a simple MSF method is not sufficient to discriminate the images. The method does not capture sufficient spatial co-occurrence information as required for large databases. To overcome the shortcoming in this research, two extended methods namely multichannel nonhomogencous MSF (MCN-MSF) and multi-resolution nonhomogeneous MSF (MRN-MSF) based on original MSF are proposed. In both cases, the concept of nonhomogeneous Markov chain model is exploited to construct the features. For the first method, we incorporate spatial co-occurrence structures of different color channels of an image by applying the time inhomogeneous (nonhomogeneous) Markov chain model. For the second method, by exploiting the similar nonhomogeneous model, we incorporate the spatial cooccu1Tence information more consistently by mapping the image with different resolution. Without compromising effectiveness and robustness, the methods proposed in this paper keeps the features level simplicity. Widely recognized WANGl000 and Corell0800 databases arc used to evaluate and compare the performances of the proposed algorithms with the existing methods. The experimental results show that both methods significantly improve the performances compare to the existing methods. The results also prove that second method is more effective for large databases.