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Segmentation Driven Object Detection with Fisher Vectors

Ramazan Gokberk Cinbis 1 Jakob Verbeek 1 Cordelia Schmid 1
1 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 present an object detection system based on the Fisher vector (FV) image representation computed over SIFT and color descriptors. For computational and storage efficiency, we use a recent segmentation-based method to generate class-independent object detection hypotheses, in combination with data compression techniques. Our main contribution is a method to produce tentative object segmentation masks to suppress background clutter in the features. Re-weighting the local image features based on these masks is shown to improve object detection significantly. We also exploit contextual features in the form of a full-image FV descriptor, and an inter-category rescoring mechanism. Our experiments on the VOC 2007 and 2010 datasets show that our detector improves over the current state-of-the-art detection results.
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Submitted on : Monday, February 10, 2014 - 4:12:21 PM
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Ramazan Gokberk Cinbis, Jakob Verbeek, Cordelia Schmid. Segmentation Driven Object Detection with Fisher Vectors. ICCV - IEEE International Conference on Computer Vision, Dec 2013, Sydney, Australia. pp.2968-2975, ⟨10.1109/ICCV.2013.369⟩. ⟨hal-00873134v2⟩



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