Combining Color and Shape Information for Appearance-Based Object Recognition Using Ultrametric Spin Glass-Markov Random Fields

Shape and color information are important cues for object recognition. An ideal system should give the option to use both forms of information, as well as the option to use just one of the two. We present in this paper a kernel method that achieves this goal. It is based on results of statistical physics ofd isordered systems combined with Gibbs distributions via kernel functions. Experimental results on a database of 100 objects confirm the effectiveness of the proposed approach.

[1]  Hiroshi Murase,et al.  Learning and recognition of 3D objects from appearance , 1993, [1993] Proceedings IEEE Workshop on Qualitative Vision.

[2]  M. Mézard,et al.  Spin Glass Theory and Beyond , 1987 .

[3]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[4]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[5]  Tomaso A. Poggio,et al.  Image representations for object detection using kernel classifiers , 2000 .

[6]  Massimiliano Pontil,et al.  Support Vector Machines for 3D Object Recognition , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Horst Bischof,et al.  Robust Recognition Using Eigenimages , 2000, Comput. Vis. Image Underst..

[8]  Martial Hebert,et al.  Object Representation in Computer Vision II , 1996, Lecture Notes in Computer Science.

[9]  Michael J. Swain,et al.  Color indexing , 1991, International Journal of Computer Vision.

[10]  Bartlett W. Mel.  SEEMORE: Combining Color, Shape, and Texture Histogramming in a Neurally Inspired Approach to Visual Object Recognition , 1997, Neural Computation.

[11]  Federico Girosi,et al.  Training support vector machines: an application to face detection , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[12]  Katsushi Ikeuchi,et al.  Sensor Modeling, Probabilistic Hypothesis Generation, and Robust Localization for Object Recognition , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  James W. Modestino,et al.  A Markov Random Field Model-Based Approach To Image Interpretation , 1989, Other Conferences.

[14]  Jiri Matas,et al.  On representation and matching of multi-coloured objects , 1995, Proceedings of IEEE International Conference on Computer Vision.

[15]  Heinrich Niemann,et al.  A Spin-Glass Markov Random Field for 3-D Object Recognition , 2002 .

[16]  M.M. Van Hulle,et al.  View-based 3D object recognition with support vector machines , 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).

[17]  Bernt Schiele,et al.  Recognition without Correspondence using Multidimensional Receptive Field Histograms , 2004, International Journal of Computer Vision.

[18]  Tapas Kanungo,et al.  Object recognition using appearance-based parts and relations , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[19]  Sameer A. Nene,et al.  Columbia Object Image Library (COIL100) , 1996 .

[20]  Anil K. Jain,et al.  Three-Dimensional Object Recognition Systems , 1993 .

[21]  Glenn Healey,et al.  Combining color and geometric information for the illumination invariant recognition of 3-D objects , 1995, Proceedings of IEEE International Conference on Computer Vision.

[22]  Alexander J. Smola,et al.  Learning with kernels , 1998 .

[23]  Martial Hebert,et al.  Object Representation in Computer Vision , 1994, Lecture Notes in Computer Science.

[24]  J. Hopfield.  Neurons withgraded response havecollective computational properties likethoseoftwo-state neurons , 1984 .

[25]  Daniel J. Amit,et al.  Modeling brain function: the world of attractor neural networks, 1st Edition , 1989 .

[26]  Rajesh P. N. Rao,et al.  An Active Vision Architecture Based on Iconic Representations , 1995, Artif. Intell..