Shape classification using the inner-distance
Shape classification using the inner-distance
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DOI：
10.1109/tpami.2007.41
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发表时间：
2007-02-01
影响因子：
23.6
通讯作者：
Jacobs, David W.
中科院分区：
文献类型：
--
作者：
Ling, Haibin;Jacobs, David W.
部件结构和连接在计算机视觉和人类视觉中至关重要。我们提议使用内距离来构建对连接具有鲁棒性并能捕捉部件结构的形状描述符。内距离被定义为形状轮廓内标志点之间最短路径的长度。我们表明它对连接不敏感，并且在捕捉部件结构方面比欧几里得距离更有效。这表明内距离可替代欧几里得距离来为复杂形状构建更准确的描述符，特别是对于那些具有连接部件的形状。此外，沿着最短路径的纹理信息可用于进一步改进形状分类。基于此想法，我们提出了三种使用内距离的方法。第一种方法将内距离和多维缩放（MDS）相结合，为连接形状构建连接不变特征。第二种方法使用内距离基于形状上下文构建一种新的形状描述符。第三种方法通过考虑沿着最短路径的纹理信息对第二种方法进行了扩展。所提出的方法已经在多种形状数据库上进行了测试，包括一个连接形状数据集、MPEG7 CE - Shape - 1、基米亚轮廓、ETH - 80数据集、两个树叶数据集以及一个人体运动轮廓数据集。在所有实验中，与其他算法相比，我们的方法都展示出了有效的性能。
Part structure and articulation are of fundamental importance in computer and human vision. We propose using the inner-distance to build shape descriptors that are robust to articulation and capture part structure. The inner-distance is defined as the length of the shortest path between landmark points within the shape silhouette. We show that it is articulation insensitive and more effective at capturing part structures than the Euclidean distance. This suggests that the inner-distance can be used as a replacement for the Euclidean distance to build more accurate descriptors for complex shapes, especially for those with articulated parts. In addition, texture information along the shortest path can be used to further improve shape classification. With this idea, we propose three approaches to using the inner-distance. The first method combines the inner-distance and multidimensional scaling (MDS) to build articulation invariant signatures for articulated shapes. The second method uses the inner-distance to build a new shape descriptor based on shape contexts. The third one extends the second one by considering the texture information along shortest paths. The proposed approaches have been tested on a variety of shape databases, including an articulated shape data set, MPEG7 CE-Shape-1, Kimia silhouettes, the ETH-80 data set, two leaf data sets, and a human motion silhouette data set. In all the experiments, our methods demonstrate effective performance compared with other algorithms.