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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Sketchable Histograms of Oriented Gradients for Object Detection
verfasst von : Ekaterina Zaytseva, Santi Seguí, Jordi Vitrià
Erschienen in: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Verlag: Springer Berlin Heidelberg
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In this paper we investigate a new representation approach for visual object recognition. The new representation, called sketchable-HoG, extends the classical histogram of oriented gradients (HoG) feature by adding two different aspects: the
stability
of the majority orientation and the
continuity
of gradient orientations. In this way, the sketchable-HoG locally characterizes the complexity of an object model and introduces global structure information while still keeping simplicity, compactness and robustness. We evaluated the proposed image descriptor on publicly Catltech 101 dataset. The obtained results outperforms classical HoG descriptor as well as other reported descriptors in the literature.