TY - GEN
T1 - Sparse patch coding for 3D model retrieval
AU - Liu, Zhenbao
AU - Bu, Shuhui
AU - Han, Junwei
AU - Wu, Jun
PY - 2014
Y1 - 2014
N2 - 3D shape retrieval is a fundamental task in many domains such as multimedia, graphics, CAD, and amusement. In this paper, we propose a 3D object retrieval approach by effectively utilizing low-level patches with initial semantics of 3D shapes, which are similar as superpixels in images. These patches are first obtained by means of stably over-segmenting 3D shape, and we adopt five representative geometric features such as shape diameter function, average geodesic distance, and heat kernel signature, to characterize these low-level patches. A large number of patches collected from shapes in a dataset are encoded into visual words by virtue of sparse coding, and input query compares with 3D models in the dataset by probability distribution of visual words. Experiments show that the proposed method achieves comparable retrieval performance to state-of-the-art methods.
AB - 3D shape retrieval is a fundamental task in many domains such as multimedia, graphics, CAD, and amusement. In this paper, we propose a 3D object retrieval approach by effectively utilizing low-level patches with initial semantics of 3D shapes, which are similar as superpixels in images. These patches are first obtained by means of stably over-segmenting 3D shape, and we adopt five representative geometric features such as shape diameter function, average geodesic distance, and heat kernel signature, to characterize these low-level patches. A large number of patches collected from shapes in a dataset are encoded into visual words by virtue of sparse coding, and input query compares with 3D models in the dataset by probability distribution of visual words. Experiments show that the proposed method achieves comparable retrieval performance to state-of-the-art methods.
KW - 3D object retrieval
KW - Patch
KW - Sparse coding
UR - http://www.scopus.com/inward/record.url?scp=84893448337&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-04117-9_11
DO - 10.1007/978-3-319-04117-9_11
M3 - 会议稿件
AN - SCOPUS:84893448337
SN - 9783319041162
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 116
EP - 127
BT - MultiMedia Modeling - 20th Anniversary International Conference, MMM 2014, Proceedings
T2 - 20th Anniversary International Conference on MultiMedia Modeling, MMM 2014
Y2 - 6 January 2014 through 10 January 2014
ER -