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Labeling, discovering, and detecting objects in images

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dc.contributor.advisor William T. Freeman. en_US Russell, Bryan Christopher, 1979- en_US
dc.contributor.other Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. en_US 2008-11-07T18:57:14Z 2008-11-07T18:57:14Z 2008 en_US 2008 en_US
dc.description Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008. en_US
dc.description Includes bibliographical references (p. 131-138). en_US
dc.description.abstract Recognizing the many objects that comprise our visual world is a difficult task. Confounding factors, such as intra-class object variation, clutter, pose, lighting, dealing with never-before seen objects, scale, and lack of visual experience often fool existing recognition systems. In this thesis, we explore three issues that address a few of these factors: the importance of labeled image databases for recognition, the ability to discover object categories from simply looking at many images, and the use of large labeled image databases to efficiently detect objects embedded in scenes. For each of the issues above, we will need to cope with large collections of images. We begin by introducing LabelMe, a large labeled image database collected from users via a web annotation tool. The users of the annotation tool provided information about the identity, location, and extent of objects in images. Through this effort, we have collected about 160,000 images and 200,000 object labels to date. We show that the database spans more object categories and scenes and offers a wider range of appearance variation than most other labeled databases for object recognition. We also provide four useful extensions of the database: (i) resolving synonym ambiguities that arise in the object labels, (ii) recovering object-part relationships, (iii) extracting a depth ordering of the labeled objects in an image, and (iv) providing a semi-automatic process for the fast labeling of images. We then seek to learn models of objects in the extreme case when no supervision is provided. We draw inspiration from the success of unsupervised topic discovery in text. We apply the Latent Dirichlet Allocation model of Blei et al. to unlabeled images to automatically discover object categories. To achieve this, we employ the visual words representation of images, which is analogous to the words in text. en_US
dc.description.abstract (cont) We show that our unsupervised model achieves comparable classification performance to a model trained with supervision on an unseen image set depicting several object classes. We also successfully localize the discovered object classes in images. While the image representation used for the object discovery process is simple to compute and can distinguish between different object categories, it does not capture explicit spatial information about regions in different parts of the image. We describe a procedure for combining image segmentation with the object discovery process to en_US
dc.description.statementofresponsibility by Bryan Christopher Russell. en_US
dc.format.extent 138 p. en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. en_US
dc.rights.uri en_US
dc.subject Electrical Engineering and Computer Science. en_US
dc.title Labeling, discovering, and detecting objects in images en_US
dc.type Thesis en_US Ph.D. en_US
dc.contributor.department Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. en_US
dc.identifier.oclc 243866111 en_US

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