PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Using multiple segmentations to discover objects and their extent in image collections
B. Russell, A. Efros, Josef Sivic, W. Freeman and Andrew Zisserman
In: CVPR 2006, 17-22 June 2006, New York, USA.


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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EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:2118
Deposited By:Mudigonda Pawan Kumar
Deposited On:02 June 2006