Histogram mining based on Markov chain and its application to image categorization

Histogram is a useful feature for image content analysis and has been widely used in many methods for image categorization. Most of the existing classifiers usually cannot distinguish the effects of different bins in histogram, except for setting different weights. However, these weights are often difficult to be exactly determined in advance. To further mine the information in histogram, in this paper, we propose a method to represent the histogram in another form called quasi-histogram, which can be thought as the state sequence of a Markov chain (MC). By modeling the quasi-histogram of each image as having been stochastically generated by an MC corresponding to its category, we can take the characteristic of each bin into account. Improved image categorization performance can be obtained through combining the results of the traditional classifier with those of MC. Experimental results show the effectiveness of our proposal.