Structural Descriptors for Category Level Object Detection

We propose a new class of descriptors which exhibits the ability to yield meaningful structural descriptions of objects. These descriptors are constructed from two types of image primitives: quadrangles and ellipses. The primitives are extracted from an image based on human cognitive psychology and model local parts of objects. Experiments reveal that these primitives densely cover objects in images. In this regard, structural information of an object can be comprehensively described by these primitives. It is found that a combination of simple spatial relationships between primitives plus a small set of geometrical attributes provide rich and accurate local structural descriptions of objects. Category level object detection of four-legged animals, bicycles, and cars images is demonstrated under scaling, moderate viewpoint variations, and background clutter. Promising results are achieved.