Effective Multi-level Image Representation for Image Categorization

This paper proposes a novel approach for image categorization based on effective multi-level image representation (MLIR). On one hand, to exploit fully the information of segmented regions at different levels in the image, we recursively segment the image into a hierarchical structure. On the other hand, to represent the information at different levels in a uniform manner, we construct a visual vocabulary based on the image regions of the hierarchical structure by a random sampling strategy. And the intermediate feature mapping is adopted to form a multi-level image representation, which encodes the information of the image at different levels, and can be very useful for distinguishing images from different categories. Experimental results on the widely used COREL data set have shown our proposed approach can achieve significant improvement compared with the state-of-the-art methods.