A shape-based object class detection model using local scale-invariant fragment feature

Detecting object in unseen images is an challenging task because of the strong clutter background, various scale of object and the deformation of class. In this paper, we present a shape-based object detection model using scale-invariant fragment feature which is approximated by conjunctive short straight segments. This is a novel shape descriptor for object detection by bypassing estimation of scale of object in natural scene. Utilizing those local and consistent segments, we improve the robustness of model to natural background and deformation of object. We experiment our model on two texture-less image datasets, INRIA horses dataset and Weizmann horses dataset. The results demonstrate our model outperform those state-of-the-art methods.