Robust Semantic Template Matching Using a Superpixel Region Binary Descriptor

Almost all conventional template-matching methods employ low-level image features to measure the similarity between a template image and a scene image using similarity measures, such as pixel intensity and pixel gradient. Although these methods have been widely used in many applications, they cannot simultaneously address all types of robustness challenges. In this paper, with the goal of simultaneously addressing the various challenges, we present a robust semantic template-matching (RSTM) approach. Inspired by the local binary descriptor, we propose a novel superpixel region binary descriptor (SRBD) to construct a multilevel semantic fusion feature vector for RSTM. SRBD uses a new kernel-distance-based simple linear iterative clustering method to extract the stable superpixels from the template image. Then, based on the average intensity difference between each superpixel region and its neighbors, the dominant gradient orientation of each superpixel can be obtained, and the semantic features of each superpixel can be described as the dominant orientation difference vector, which is coded as the rotation-invariant SRBD. In the offline matching phase, the fusion semantic feature vector of RSTM combines the multilevel SRBD features with different numbers of superpixels. In the online matching phase, to cope with rotation invariance, a marginal probability model is proposed and applied to locate the positions of template images in the scene image. Moreover, to accelerate computation, an image pyramid is employed. We conduct a series of experiments on a large dataset randomly selected from the MS COCO dataset to fully analyze the robustness of this approach. The experimental results show that RSTM simultaneously addresses rotation changes, scale changes, noise, occlusions, blur, nonlinear illumination changes, and deformation with high time efficiency while also outperforming the previous state-of-the-art template-matching methods.