Using Multiple Segmentations to Discover Objects and their Extent in Image Collections
journal contributionposted on 01.01.2006 by Bryan C. Russell, Alexei A Efros, Josef Sivic, William T. Freeman, Andrew Zisserman
Any type of content formally published in an academic journal, usually following a peer-review process.
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.