Effective Feature Description Using Intensity Order Local Binary Pattern

This paper presents an effective feature descriptor that integrates intensity order and textures in multi support regions into a compact vector. We first propose the novel Intensity Order Local Binary Pattern (IO-LBP) to encode the texture around each point in an interest region, divide the region according to pixel intensity orders, and then pool patterns to these segments. The IO-LBP descriptor is built by concatenating histograms of all segments together. Besides, multi support regions are used to further improve the discriminative ability. The proposed descriptor can effectively capture both local and global information of an interest region and thus high performance is expected. We evaluate IO-LBP on the standard Oxford dataset and additional images of shadows. Experimental results show that our method is not only invariant to common photometric and geometric transformations, such as illumination change, image rotation, but also robust to complex illumination effects caused by shadows. A significant improvement in performance, comparing to state-of-the-art descriptors, is achieved by IO-LBP.