关键词: 局部描述符, 关键点, 尺度不变特征变换, 形状上下文, 结构上下文

Abstract: Local descriptors， such as SIFT （scale invariant feature transform）， encode the local appearance information of the interest points as image features with rotation， scale and viewpoint invariance. This method is widely used in many fields including object categorization， object recognition and image matching， etc. However， the biggest problem for most local descriptors is that they only capture the local information but ignore the geometric configuration， which is useful in describing the object. Structural context， a new local descriptor which encodes the geometric configurations between the interested points， is introduced. The experimental results prove that it is more powerful in describing the object category.

Key words: local descriptor, interest point, scale invariant feature transform （SIFT）, shape context, structural context