A cluster-based statistical model for object detection

This paper presents an approach to object detection which is based on recent work in statistical models for texture synthesis and recognition. Our method follows the texture recognition work of De Bonet and Viola (1998). We use feature vectors which capture the joint occurrence of local features at multiple resolutions. The distribution of feature vectors for a set of training images of an object class is estimated by clustering the data and then forming a mixture of Gaussian models. The mixture model is further refined by determining which clusters are the most discriminative for the class and retaining only those clusters. After the model is learned, test images are classified by computing the likelihood of their feature vectors with respect to the model. We present promising results in applying our technique to face detection and car detection.

[1]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[2]  Alex Pentland,et al.  Face recognition using eigenfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[3]  Joachim M. Buhmann,et al.  Distortion Invariant Object Recognition in the Dynamic Link Architecture , 1993, IEEE Trans. Computers.

[4]  Tomaso Poggio,et al.  Example Based Image Analysis and Synthesis , 1993 .

[5]  P. Perona,et al.  Face Localization via Shape Statistics , 1995 .

[6]  J. Bergen,et al.  Pyramid-based texture analysis/synthesis , 1995, SIGGRAPH.

[7]  William T. Freeman,et al.  Presented at: 2nd Annual IEEE International Conference on Image , 1995 .

[8]  Takeo Kanade,et al.  Human Face Detection in Visual Scenes , 1995, NIPS.

[9]  Alex Pentland,et al.  Bayesian face recognition using deformable intensity surfaces , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[10]  Paul A. Viola.  Complex Feature Recognition: A Bayesian Approach for Learning to Recognize Objects , 1996 .

[11]  Jeremy S. De Bonet,et al.  Multiresolution sampling procedure for analysis and synthesis of texture images , 1997, SIGGRAPH.

[12]  Kris Popat,et al.  Cluster-based probability model and its application to image and texture processing , 1997, IEEE Trans. Image Process..

[13]  Eero P. Simoncelli,et al.  Embedded wavelet image compression based on a joint probability model , 1997, Proceedings of International Conference on Image Processing.

[14]  Norbert Krüger,et al.  Face Recognition by Elastic Bunch Graph Matching , 1997, CAIP.

[15]  Tomaso A. Poggio,et al.  Example-Based Learning for View-Based Human Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Paul A. Viola,et al.  Texture recognition using a non-parametric multi-scale statistical model , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[17]  Eero P. Simoncelli,et al.  Texture characterization via joint statistics of wavelet coefficient magnitudes , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[18]  Tomaso A. Poggio,et al.  A general framework for object detection , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[19]  Song-Chun Zhu.  Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling , 1998 .

[20]  Takeo Kanade,et al.  Probabilistic modeling of local appearance and spatial relationships for object recognition , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[21]  Timothy F. Cootes,et al.  Interpreting face images using active appearance models , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.