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.
SUMMARYThis paper introduces a novel approach of feature description by integrating the intensity order and textures in different support regions into a compact vector. We first propose the Intensity Order Local Binary Pattern (IO-LBP) operator, which simultaneously encodes the gradient and texture information in the local neighborhood of a pixel. We divide each region of interest into segments according to the order of pixel intensities, build one histogram of IO-LBP patterns for each segment, and then concatenate all histograms to obtain a feature descriptor. Furthermore, multi support regions are adopted to enhance the distinctiveness. The proposed descriptor effectively describes a region at both local and global levels, and thus high performance is expected. Experimental results on the Oxford benchmark and images of cast shadows show that our approach is invariant to common photometric and geometric transformations, such as illumination change and image rotation, and robust to complex lighting effects caused by shadows. It achieves a comparable accuracy to that of state-of-art methods while performs considerably faster.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.