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PAMI
2012

Unsupervised Learning of Categorical Segments in Image Collections

6 years 7 months ago
Unsupervised Learning of Categorical Segments in Image Collections
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.
Marco Andreetto, Lihi Zelnik-Manor, Pietro Perona
Added 28 Sep 2012
Updated 28 Sep 2012
Type Journal
Year 2012
Where PAMI
Authors Marco Andreetto, Lihi Zelnik-Manor, Pietro Perona
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