부산대학교 도서관

Robust image features are essential in building effective image recognition engines. These features can be constructed according to various principles, such the distribution of local gradients (Histogram of Oriented Gradients, HOG), the relationship between two pixels (Local Binary Descriptors, LBD), or local intensity order statistics (Local Intensity Order Patterns, LIOP). Because the feature dimension grows quickly as one considers the ordering relations of a group of N (N>2) pixels, few researchers have exploited local order statistics among a pixel set to define an image feature. In this paper, we propose a novel approach to construct a feature descriptor using local intensity order relations (LIOR) in a pixel group. In contrast to LIOP where the feature dimension increases drastically with the number of elements in a set, the size of LIOR is manageable. Moreover, LIOR ensures the stability of ordering by encoding the intensity differences as weights. Two different strategies for assigning the weights have been devised and tested. Experimental results indicate that the proposed methods yield better or comparable performance for different types of image degradation when compared to the original LIOP. Additionally, the storage requirement is significantly lower when the number of pixels in a group increases.