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, INPG - Institut National Polytechnique de Grenoble
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.
Type de document :
Communication dans un congrès
ICCV - IEEE International Conference on Computer Vision, Dec 2013, Sydney, Australia. IEEE, pp.2968-2975, 2013, 〈10.1109/ICCV.2013.369〉
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https://hal.inria.fr/hal-00873134
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Soumis le : lundi 10 février 2014 - 16:12:21
Dernière modification le : mardi 26 septembre 2017 - 01:25:20
Document(s) archivé(s) le : dimanche 9 avril 2017 - 10:18:10

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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. IEEE, pp.2968-2975, 2013, 〈10.1109/ICCV.2013.369〉. 〈hal-00873134v2〉

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