Content-based 3D object retrieval has be- come an active topic in many research communities. In this paper, we propose a novel visual similarity based 3Dshaperetrievalmethod(CM-BOF)usingClockMatch- ing and Bag-of-Features. Specifically, pose normaliza- tion is first applied to each object to generate its canon- ical pose, and then the normalized object is represent- ed by a set of depth-buffer images captured on the vertices of a given geodesic sphere. Afterwards, each image is described as a word histogram obtained by the vector quantization of the images salient local fea- tures. Finally, an efficient multi-view shape matching scheme (i.e., Clock Matching) is employed to measure the dissimilarity between two models. When applying the CM-BOF method in non- rigid 3D shape retrieval, Multidimensional Scaling (MDS) should be utilized be- fore pose normalization to calculate the canonical for- m for each object. This paper also investigates several critical issues for the CM-BOF method, including the influence of the number of views, codebook, training data, and distance function. Experimental results on five commonly-used benchmarks demonstrate that: 1) In contrast to the traditional Bag-of-Features, the time- consumingclusteringisnotnecessaryforthecodebook construction of the CM-BOF approach; 2) Our meth- ods are superior or comparable to the state of the art in applications of both rigid and non-rigid 3D shape retrieval.
Citation: Machine Vision and Applications
Pub Type: Journals
3D Shape Retrieval , Non-rigid , Bag-of- Features , Local Feature