CAJ | 학술논문

局部描述符(如SIFT)方法能够将图像中关键点的局部表观信息作为图像的特征,具有旋转不变性、尺度变换不变性、仿射不变性等性质,被广泛应用于物体分类、物体识别、图像匹配等领域.但是,它存在一个重要缺陷:只能描述物体的局部特征,忽略了整个物体的构造,而这在表示物体时是非常重要的.设计了一个新的"结构上下文"局部描述符,通过当前关键点和其他关键点间的空间拓扑结构关系描述各个关键点的特征.实验证明这种描述符在描述相同物体种类时特别有效.
국부묘술부(여SIFT)방법능구장도상중관건점적국부표관신식작위도상적특정,구유선전불변성、척도변환불변성、방사불변성등성질,피엄범응용우물체분류、물체식별、도상필배등영역.단시,타존재일개중요결함:지능묘술물체적국부특정,홀략료정개물체적구조,이저재표시물체시시비상중요적.설계료일개신적"결구상하문"국부묘술부,통과당전관건점화기타관건점간적공간탁복결구관계묘술각개관건점적특정.실험증명저충묘술부재묘술상동물체충류시특별유효.
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.