Machine Learning
Introduction to MPEG-7: Multimedia Content Description Interface
Introduction to MPEG-7: Multimedia Content Description Interface
Towards Stable and Salient Multi-View Representation of 3D Shapes
SMI '06 Proceedings of the IEEE International Conference on Shape Modeling and Applications 2006
Data complexity in machine learning and novel classification algorithms
Data complexity in machine learning and novel classification algorithms
A Compact Multi-view Descriptor for 3D Object Retrieval
CBMI '09 Proceedings of the 2009 Seventh International Workshop on Content-Based Multimedia Indexing
A Study and Application on Machine Learning of Artificial Intellligence
JCAI '09 Proceedings of the 2009 International Joint Conference on Artificial Intelligence
Image analysis by Krawtchouk moments
IEEE Transactions on Image Processing
MPEG-7 visual shape descriptors
IEEE Transactions on Circuits and Systems for Video Technology
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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%.