PT Unknown
AU Ekaterina Zaytseva
   Santiago Segui
   Jordi Vitria
TI Sketchable Histograms of Oriented Gradients for Object Detection
BT 17th Iberomerican Conference on Pattern Recognition
PY 2012
BP 374
EP 381
VL 7441
DI 10.1007/978-3-642-33275-3_46
AB 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.
ER