Weakly supervised object localization with stable segmentations
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Weakly supervised object localization with stable segmentations. / Galleguillos, Carolina; Babenko, Boris; Rabinovich, Andrew; Belongie, Serge.
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), No. PART 1, 2008, p. 193-207.Research output: Contribution to journal › Conference article › Research › peer-review
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TY - GEN
T1 - Weakly supervised object localization with stable segmentations
AU - Galleguillos, Carolina
AU - Babenko, Boris
AU - Rabinovich, Andrew
AU - Belongie, Serge
PY - 2008
Y1 - 2008
N2 - Multiple Instance Learning (MIL) provides a framework for training a discriminative classifier from data with ambiguous labels. This framework is well suited for the task of learning object classifiers from weakly labeled image data, where only the presence of an object in an image is known, but not its location. Some recent work has explored the application of MIL algorithms to the tasks of image categorization and natural scene classification. In this paper we extend these ideas in a framework that uses MIL to recognize and localize objects in images. To achieve this we employ state of the art image descriptors and multiple stable segmentations. These components, combined with a powerful MIL algorithm, form our object recognition system called MILSS. We show highly competitive object categorization results on the Caltech dataset. To evaluate the performance of our algorithm further, we introduce the challenging Landmarks-18 dataset, a collection of photographs of famous landmarks from around the world. The results on this new dataset show the great potential of our proposed algorithm.
AB - Multiple Instance Learning (MIL) provides a framework for training a discriminative classifier from data with ambiguous labels. This framework is well suited for the task of learning object classifiers from weakly labeled image data, where only the presence of an object in an image is known, but not its location. Some recent work has explored the application of MIL algorithms to the tasks of image categorization and natural scene classification. In this paper we extend these ideas in a framework that uses MIL to recognize and localize objects in images. To achieve this we employ state of the art image descriptors and multiple stable segmentations. These components, combined with a powerful MIL algorithm, form our object recognition system called MILSS. We show highly competitive object categorization results on the Caltech dataset. To evaluate the performance of our algorithm further, we introduce the challenging Landmarks-18 dataset, a collection of photographs of famous landmarks from around the world. The results on this new dataset show the great potential of our proposed algorithm.
UR - http://www.scopus.com/inward/record.url?scp=56749180633&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-88682-2_16
DO - 10.1007/978-3-540-88682-2_16
M3 - Conference article
AN - SCOPUS:56749180633
SP - 193
EP - 207
JO - Lecture Notes in Computer Science
JF - Lecture Notes in Computer Science
SN - 0302-9743
IS - PART 1
T2 - 10th European Conference on Computer Vision, ECCV 2008
Y2 - 12 October 2008 through 18 October 2008
ER -
ID: 302050565