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Multiple Component Learning for Object Detection

Dollár, Piotr and Babenko, Boris and Belongie, Serge and Perona, Pietro and Tu, Zhuowen (2008) Multiple Component Learning for Object Detection. In: Computer Vision – ECCV 2008. Lecture Notes in Computer Science. No.5303. Springer , Berlin, pp. 211-224. https://resolver.caltech.edu/CaltechAUTHORS:20140730-101716706

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Abstract

Object detection is one of the key problems in computer vision. In the last decade, discriminative learning approaches have proven effective in detecting rigid objects, achieving very low false positives rates. The field has also seen a resurgence of part-based recognition methods, with impressive results on highly articulated, diverse object categories. In this paper we propose a discriminative learning approach for detection that is inspired by part-based recognition approaches. Our method, Multiple Component Learning (MCL), automatically learns individual component classifiers and combines these into an overall classifier. Unlike previous methods, which rely on either fairly restricted part models or labeled part data, MCL learns powerful component classifiers in a weakly supervised manner, where object labels are provided but part labels are not. The basis Of MCL lies in learning a set classifier; we achieve this by combining boosting with weakly supervised learning, specifically the Multiple Instance Learning framework (MIL). MCL is general, and we demonstrate results on a range of data from computer audition and computer vision. In particular, MCL outperforms all existing methods on the challenging INRIA pedestrian detection dataset, and unlike methods that are not part-based, MCL is quite robust to occlusions.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1007/978-3-540-88688-4_16DOIArticle
http://link.springer.com/chapter/10.1007/978-3-540-88688-4_16PublisherArticle
ORCID:
AuthorORCID
Belongie, Serge0000-0002-0388-5217
Perona, Pietro0000-0002-7583-5809
Additional Information:© 2008 Springer. PD and BB were funded by NSF IGERT Grant DGE-0333451. SB was funded by NSF Career Grant #0448615 and the Alfred P. Sloan Research Fellowship. ZT was funded by NIH Grant U54RR021813 entitled Center for Computational Biology.
Funders:
Funding AgencyGrant Number
NSF Graduate Research FellowshipDGE-0333451
NSFIIS-0448615
Alfred P. Sloan FoundationUNSPECIFIED
NIHU54RR021813
Series Name:Lecture Notes in Computer Science
Issue or Number:5303
Record Number:CaltechAUTHORS:20140730-101716706
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20140730-101716706
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:47594
Collection:CaltechAUTHORS
Deposited By: Caroline Murphy
Deposited On:22 Aug 2014 23:56
Last Modified:03 Oct 2019 06:55

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