Histograms of Pattern Sets for Image Classification and Object Recognition

Winn Voravuthikunchai 1 Bruno Crémilleux 2 Frédéric Jurie 1, *
* Corresponding author
1 Equipe Image - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
2 Equipe CODAG - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
Abstract : This paper introduces a novel image representation capturing feature dependencies through the mining of meaningful combinations of visual features. This representation leads to a compact and discriminative encoding of images that can be used for image classification, object detection or object recognition. The method relies on (i) multiple random projections of the input space followed by local binarization of projected histograms encoded as sets of items, and (ii) the representation of images as Histograms of Pattern Sets (HoPS). The approach is validated on four publicly available datasets (Daimler Pedestrian, Oxford Flowers, KTH Texture and PASCAL VOC2007), allowing comparisons with many recent approaches. The proposed image representation reaches state-of-the-art performance on each one of these datasets.
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Conference papers
IEEE Conference on Computer Vision and Pattern Recognition, Jun 2014, Columbus, Ohio., United States. pp.224-231, 2014
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Winn Voravuthikunchai, Bruno Crémilleux, Frédéric Jurie. Histograms of Pattern Sets for Image Classification and Object Recognition. IEEE Conference on Computer Vision and Pattern Recognition, Jun 2014, Columbus, Ohio., United States. pp.224-231, 2014. 〈hal-00980894〉

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