Using Multiple Segmentations to Discover Objects and their Extent in Image Collections
Russell, B.C.; Freeman, W.T.; Efros, A.A.; Sivic, J.; Zisserman, A.
Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on
Volume 2, Issue , 2006 Page(s): 1605 - 1614
Digital Object Identifier   10.1109/CVPR.2006.326
Summary: Given a large dataset of images, we seek to automatically determine the visually similar object and scene classes together with their image segmentation. To achieve this we combine two ideas: (i) that a set of segmented objects can be partitioned into visual object classes using topic discovery models from statistical text analysis; and (ii) that visual object classes can be used to assess the accuracy of a segmentation. To tie these ideas together we compute multiple segmentations of each image and then: (i) learn the object classes; and (ii) choose the correct segmentations. We demonstrate that such an algorithm succeeds in automatically discovering many familiar objects in a variety of image datasets, including those from Caltech, MSRC and LabelMe.

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