Gradient histogram Markov stationary features for image retrieval

This paper presents a novel framework for image retrieval. In image feature extraction stage, we propose the gradient histogram Markov stationary features to represent the input image which is capable of characterizing the spatial co-occurrence of gradient histogram patterns. In image retrieval stage, the image training and retrieval process is treated as searching for an ordered optimal cycle in the image database by minimizing the geometric manifold entropy of images. Experimental results demonstrate that the proposed framework for image retrieval is feasible and gradient histogram Markov stationary feature apparently outperforms the original HOG descriptor in feature representation.