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2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Unsupervised learning of categorical segments in image collections
Anchorage, AK, USA
June 23-June 28
ISBN: 978-1-4244-2339-2
Marco Andreetto, Dept. of Electrical Engineering, California Institute of Technology, Pasadena, 91125, United States
Lihi Zelnik-Manor, Dept. of Electrical Engineering, Technion - Israel Institute of Technology, Haifa, 32000, Israel
Pietro Perona, Dept. of Electrical Engineering, California Institute of Technology, Pasadena, 91125, United States
Which one comes first: segmentation or recognition? We propose a probabilistic framework for carrying out the two simultaneously. The framework combines an LDA ‘bag of visual words’ model for recognition, and a hybrid parametric-nonparametric model for segmentation. If applied to a collection of images, our framework can simultaneously discover the segments of each image, and the correspondence between such segments. Such segments may be thought of as the ‘parts’ of corresponding objects that appear in the image collection. Thus, the model may be used for learning new categories, detecting/classifying objects, and segmenting images.
Citation:
Marco Andreetto, Lihi Zelnik-Manor, Pietro Perona, "Unsupervised learning of categorical segments in image collections," cvprw, pp.1-8, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008
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