Image clustering using local discriminant models and global integration
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
Making image to class distance comparable
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Learning image-to-class distance metric for image classification
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on agent communication, trust in multiagent systems, intelligent tutoring and coaching systems
Journal of Visual Communication and Image Representation
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We propose a new distance measure for face recognition and human gait recognition. Each probe image (a face image or an average human silhouette image) is represented as a set of local features uniformly sampled over a grid with fixed spacing, and each gallery image is represented as a set of local features sampled at each pixel. We formulate an integer programming problem to compute the distance (referred to as the image-to-class distance) from one probe image to all the gallery images belonging to a certain class, in which any feature of the probe image can be matched to only one feature from one of the gallery images. Considering computational efficiency as well as the fact that face images or average human silhouette images are roughly aligned in the preprocessing step, we also enforce a spatial neighborhood constraint by only allowing neighboring features that are within a given spatial distance to be considered for feature matching. The integer programming problem is further treated as a classical minimum-weight bipartite graph matching problem, which can be efficiently solved with the Kuhn-Munkres algorithm. We perform comprehensive experiments on three benchmark face databases: 1) the CMU PIE database; 2) the FERET database; and 3) the FRGC database, as well as the USF Human ID gait database. The experiments clearly demonstrate the effectiveness of our image-to-class distance.