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Conference Papers Year : 2013

Segmentation Driven Object Detection with Fisher Vectors

Ramazan Gokberk Cinbis
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Jakob Verbeek
Cordelia Schmid
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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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Dates and versions

hal-00873134 , version 1 (15-10-2013)
hal-00873134 , version 2 (10-02-2014)



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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