Hierarchical bag-of-features for hand posture recognition

We study the question of hand posture recognition by developing a new class of Bag-of-Features (BoF), namely Hierarchical BoF. The Hierarchical BoF captures spatial information by dividing whole hand area into several sub-regions and projecting local features onto horizontal and vertical directions. A similarity measurement based on Histogram Intersection Kernel (HIK) is proposed to classify hand postures. According to experimental result in Section 4, Hierarchical BoF works well in hand posture recognition task owing to the following properties: 1) the model captures spatial information of each component of hand postures; 2) the model has good adaptability for various complex background conditions.