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Green Image
Issue No. 09 - Sept. (2012 vol. 34)
ISSN: 0162-8828
pp: 1842-1855
M. Andreetto , Google Los Angeles (US-LAX-BIN), Venice, CA, USA
L. Zelnik-Manor , Dept. of Electr. Eng., Technion - Israel Inst. of Technol., Haifa, Israel
P. Perona , Dept. of Electr. Eng., California Inst. of Technol., Pasadena, CA, USA
Which one comes first: segmentation or recognition? We propose a unified framework for carrying out the two simultaneously and without supervision. The framework combines a flexible probabilistic model, for representing the shape and appearance of each segment, with the popular “bag of visual words” model for recognition. If applied to a collection of images, our framework can simultaneously discover the segments of each image and the correspondence between such segments, without supervision. Such recurring segments may be thought of as the “parts” of corresponding objects that appear multiple times in the image collection. Thus, the model may be used for learning new categories, detecting/classifying objects, and segmenting images, without using expensive human annotation.
Image segmentation, Probabilistic logic, Visualization, Shape, Image recognition, Pattern analysis, scene analysis., Computer vision, image segmentation, unsupervised object recognition, graphical models, density estimation

L. Zelnik-Manor, P. Perona and M. Andreetto, "Unsupervised Learning of Categorical Segments in Image Collections," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 34, no. , pp. 1842-1855, 2012.
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