Entropy descriptor for image classification

This paper presents a novel entropy descriptor in the sense of geometric manifolds. With this descriptor, entropy cycles can be easily designed for image classification. Minimizing this entropy leads to an optimal entropy cycle where images are connected in the semantic order. During classification, the training step is to find an optimal entropy cycle in each class. In the test step, an unknown image is grouped into a class if the entropy increase as the result of inserting the image into the cycle of this class is relatively least. The proposed approach can generalize well on difficult image classification problems where images with same objects are taken in multiple views. Experimental results show that this entropy descriptor performs well in image classification and has potential in the image-based modeling retrieval.