Skip to Main content Skip to Navigation
Conference papers

Fast and Robust Object Segmentation with the Integral Linear Classifier

David Aldavert 1 Arnau Ramisa 2, 3 Ricardo Toledo 1 Ramon Lopez de Mantaras 3
2 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
Abstract : We propose an efficient method, built on the popular Bag of Features approach, that obtains robust multiclass pixel level object segmentation of an image in less than 500ms, with results comparable or better than most state of the art methods. We introduce the Integral Linear Classifier (ILC), that can readily obtain the classification score for any image sub-window with only 6 additions and 1 product by fusing the accumulation and classification steps in a single operation. In order to design a method as efficient as possible, our building blocks are carefully selected from the quickest in the state of the art. More precisely, we evaluate the performance of three popular local descriptors, that can be very efficiently computed using integral images, and two fast quantization methods: the Hierarchical K-Means, and the Extremely Randomized Forest. Finally, we explore the utility of adding spatial bins to the Bag of Features histograms and that of cascade classifiers to improve the obtained segmentation. Our method is compared to the state of the art in the difficult Graz-02 and PASCAL 2007 Segmentation Challenge datasets.
Document type :
Conference papers
Complete list of metadata

Cited literature [26 references]  Display  Hide  Download
Contributor : Thoth Team Connect in order to contact the contributor
Submitted on : Monday, December 20, 2010 - 10:23:51 AM
Last modification on : Thursday, January 20, 2022 - 5:28:06 PM
Long-term archiving on: : Monday, March 21, 2011 - 3:26:19 AM


Files produced by the author(s)




David Aldavert, Arnau Ramisa, Ricardo Toledo, Ramon Lopez de Mantaras. Fast and Robust Object Segmentation with the Integral Linear Classifier. CVPR 2010 - 23rd IEEE Conference on Computer Vision & Pattern Recognition, Jun 2010, San Francisco, United States. pp.1046-1053, ⟨10.1109/CVPR.2010.5540098⟩. ⟨inria-00548642⟩



Les métriques sont temporairement indisponibles