Weakly supervised object localization with stable segmentations
Research output: Contribution to journal › Conference article › Research › peer-review
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.
Original language | English |
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Journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Issue number | PART 1 |
Pages (from-to) | 193-207 |
Number of pages | 15 |
ISSN | 0302-9743 |
DOIs | |
Publication status | Published - 2008 |
Externally published | Yes |
Event | 10th European Conference on Computer Vision, ECCV 2008 - Marseille, France Duration: 12 Oct 2008 → 18 Oct 2008 |
Conference
Conference | 10th European Conference on Computer Vision, ECCV 2008 |
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Country | France |
City | Marseille |
Period | 12/10/2008 → 18/10/2008 |
Sponsor | Deutsche Telekom Laboratories, EADS, et al., Inria, Microsoft Research, Ville de Marseille |
ID: 302050565