A Hybrid Object-Level/Pixel-Level Framework For Shape-based Recognition

This paper presents a technique for shape-based recognition that fuses pixellevel and object-level approaches into a unified framework. A pixel-level algorithm classifies individual pixels as belonging to a target object or clutter based on automatically-selected shape features computed in a spatial arrangement around them; an object-level algorithm classifies object-sized rectangular image regions as objects or clutter by aggregating pixel classifier scores in the regions. We train a cascade of interleaved pixel-level and objectlevel modules to quickly localize complex-shaped objects in highly cluttered scenes under arbitrary out-of-image-plane rotation. Experimental results on a large set of real, highly-cluttered images of a common object under arbitrary out of image plane rotation demonstrate improvements over cascades of strictly pixel-level modules.