Multiple Component Learning for Object Detection

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, spec...

• Type: Conference
• Year: 2008
• Where: ECCV
• Authors: Boris Babenko, Pietro Perona, Piotr Dollár, Serge J. Belongie, Zhuowen Tu
• Keywords: Computer Vision, Discriminative Learning Approaches, ECCV 2008, Instance Learning Framework, Mcl Lies, Weakly Supervised Learning, Weakly Supervised Manner