Incorporating Shape Features in an Appearance-Based Object Detection System

摘要

Most object detection techniques discussed in the literature are based solely on texture-based features that capture the global or local appearance of an object. While results indicate their ability to effectively represent an object class, these features can be detected repeatably only in the object interior, and so cannot effectively exploit the powerful recognition cue of contour. Since generic object classes can be characterized by shape and appearance, this paper has formulated a method to combine these attributes to enhance the object model. We present an approach for incorporating the recently introduced shape-based features called k-Adjacent-Segments (kAS) in our appearance-based framework based on dense SIFT features. Class-specific kAS features are detected in an arbitrary image to form a shape map that is then employed in two novel ways to augment the appearance-based technique. This is shown to improve the detection performance for all classes in the challenging 3D dataset by 3-18% and the PASCAL VOC 2006 by 5%.