3D model-based semantic categorization of still image 2D objects

Automatic classification and interpretation of objects present in 2D images is a key issue for various %computer vision applications. In particular, when considering image/video, indexing, and retrieval applications, %automatically labeling in a semantically pertinent manner/huge multimedia databases still %remains a challenge. This paper examines the issue of still image object categorization. The objective is %to associate semantic labels to the 2D objects present in natural images. The principle of the proposed %approach consists of exploiting categorized 3D model repositories to identify unknown 2D objects, based %on 2D/3D matching techniques. The authors use 2D/3D shape indexing methods, where 3D models are %described through a set of 2D views. Experimental results, carried out on both MPEG-7 and Princeton %3D models databases, show recognition rates of up to 89.2%.