Multiple component learning for object detection
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Multiple component learning for object detection. / Dollár, Piotr; Babenko, Boris; Belongie, Serge; Perona, Pietro; Tu, Zhuowen.
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), No. PART 2, 2008, p. 211-224.Research output: Contribution to journal › Conference article › Research › peer-review
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TY - GEN
T1 - Multiple component learning for object detection
AU - Dollár, Piotr
AU - Babenko, Boris
AU - Belongie, Serge
AU - Perona, Pietro
AU - Tu, Zhuowen
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=57149125420&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-88688-4_16
DO - 10.1007/978-3-540-88688-4_16
M3 - Conference article
AN - SCOPUS:57149125420
SP - 211
EP - 224
JO - Lecture Notes in Computer Science
JF - Lecture Notes in Computer Science
SN - 0302-9743
IS - PART 2
T2 - 10th European Conference on Computer Vision, ECCV 2008
Y2 - 12 October 2008 through 18 October 2008
ER -
ID: 302050497