Shape classification using the inner-distance

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Shape classification using the inner-distance (2007)

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by Haibin Ling , David W. Jacobs

Venue:	PATTERN ANALYSIS AND MACHINE INTELLIGENCE, IEEE TRANSACTIONS ON
Citations:	174 - 7 self

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@ARTICLE{Ling07shapeclassification,
    author = {Haibin Ling and David W. Jacobs},
    title = {Shape classification using the inner-distance},
    journal = {PATTERN ANALYSIS AND MACHINE INTELLIGENCE, IEEE TRANSACTIONS ON},
    year = {2007}
}

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Abstract

Part structure and articulation are of fundamental importance in computer and human vision. We propose using the innerdistance 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.

Keyphrases

shape classification texture information part structure euclidean distance second method fundamental importance shape database accurate descriptor articulated part eth-80 data set capture part structure shape descriptor shortest path articulated shape effective performance complex shape leaf data set first method new shape descriptor landmark point human motion silhouette data set multidimensional scaling mpeg7 ce-shape-1 human vision shape context articulated shape data set articulation invariant signature shape silhouette

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