Exploring Discriminative Views for 3D Object Retrieval
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- 关键词：Discriminative view ; Reverse sum ; min distance ; 3D object retrieval
- 刊名：Lecture Notes in Computer Science
- 出版年：2016
- 出版时间：2016
- 年：2016
- 卷：9516
- 期：1
- 页码：755-766
- 全文大小：855 KB
- 参考文献：1.Ansary, T.F., Daoudi, M., Vandeborre, J.P.: A bayesian 3-D search engine using adaptive views clustering. IEEE Trans. Multimedia 9(1), 78–88 (2007)CrossRef
2.Brigham, E.O., Brigham, E.: The fast Fourier transform and its applications, vol. 1. Prentice Hall Englewood Cliffs, NJ (1988)
3.Chen, D.Y., Tian, X.P., Shen, Y.T., Ouhyoung, M.: On visual similarity based 3D model retrieval. In: Computer Graphics Forum, vol. 22, pp. 223–232. Wiley Online Library (2003)
4.Cheng, J., Bian, W., Tao, D.: Locally regularized sliced inverse regression based 3D hand gesture recognition on a dance robot. Inf. Sci. 221, 274–283 (2013)CrossRef
5.Daras, P., Axenopoulos, A.: A 3D shape retrieval framework supporting multimodal queries. Int. J. Comput. Vis. 89(2–3), 229–247 (2010)CrossRef
6.Gao, Y., Dai, Q.: View-based 3-d object retrieval: challenges and approaches. IEEE Trans. Multimedia (2014)
7.Gao, Y., Dai, Q., Wang, M., Zhang, N.: 3D model retrieval using weighted bipartite graph matching. Sig. Process. Image Commun. 26(1), 39–47 (2011)CrossRef
8.Gao, Y., Dai, Q., Zhang, N.Y.: 3D model comparison using spatial structure circular descriptor. Pattern Recogn. 43(3), 1142–1151 (2010)MATH CrossRef
9.Gao, Y., Tang, J., Hong, R., Yan, S., Dai, Q., Zhang, N., Chua, T.S.: Camera constraint-free view-based 3-D object retrieval. IEEE Trans. Image Process. 21(4), 2269–2281 (2012)MathSciNet CrossRef
10.Gao, Y., Wang, M., Ji, R., Wu, X., Dai, Q.: 3-D object retrieval with hausdorff distance learning. IEEE Trans. Ind. Electron. 61(4), 2088–2098 (2014)CrossRef
11.Gao, Y., Wang, M., Tao, D., Ji, R., Dai, Q.: 3-D object retrieval and recognition with hypergraph analysis. IEEE Trans. Image Process. 21(9), 4290–4303 (2012)MathSciNet CrossRef
12.Gao, Y., Wang, M., Zha, Z.J., Tian, Q., Dai, Q., Zhang, N.: Less is more: efficient 3-D object retrieval with query view selection. IEEE Trans. Multimedia 13(5), 1007–1018 (2011)CrossRef
13.Giorgi, D., Mortara, M., Spagnuolo, M.: 3D shape retrieval based on best view selection. In: Proceedings of the ACM Workshop on 3D Object Retrieval, pp. 9–14 (2010)
14.Hong, C., Yu, J., You, J., Chen, X., Tao, D.: Multi-view ensemble manifold regularization for 3D object recognition. Inf. Sci. (2015)
15.Khotanzad, A., Hong, Y.H.: Invariant image recognition by zernike moments. IEEE Trans. Pattern Anal. Mach. Intell. 12(5), 489–497 (1990)CrossRef
16.Laga, H.: Semantics-driven approach for automatic selection of best views of 3D shapes. In: Proceedings of the 3rd Eurographics Conference on 3D Object Retrieval, pp. 15–22 (2010)
17.Leibe, B., Schiele, B.: Analyzing appearance and contour based methods for object categorization. In: Computer Vision and Pattern Recognition, vol. 2, p. II-409 (2003)
18.Liu, Q., Yang, Y., Ji, R., Gao, Y., Yu, L.: Cross-view down/up-sampling method for multiview depth video coding. Sig. Process. Lett. 19(5), 295–298 (2012)CrossRef
19.Liu, Y., Wang, X.L., Wang, H.Y., Zha, H., Qin, H.: Learning robust similarity measures for 3D partial shape retrieval. Int. J. Comput. Vis. 89(2–3), 408–431 (2010)CrossRef
20.Ohbuchi, R., Osada, K., Furuya, T., Banno, T.: Salient local visual features for shape-based 3D model retrieval. In: IEEE International Conference on Shape Modeling and Applications, pp. 93–102 (2008)
21.Shih, J.L., Lee, C.H., Wang, J.T.: A new 3D model retrieval approach based on the elevation descriptor. Pattern Recogn. 40(1), 283–295 (2007)MATH CrossRef
22.Wang, M., Hua, X.S., Hong, R., Tang, J., Qi, G.J., Song, Y.: Unified video annotation via multigraph learning. IEEE Trans. Circ. Syst. Video Technol. 19(5), 733–746 (2009)CrossRef
23.Yap, P.T., Paramesran, R., Ong, S.H.: Image analysis by Krawtchouk moments. IEEE Trans. Image Process. 12(11), 1367–1377 (2003)MathSciNet CrossRef
24.Zhao, S., Chen, L., Yao, H., Zhang, Y., Sun, X.: Strategy for dynamic 3D depth data matching towards robust action retrieval. Neurocomputing 151, 533–543 (2015)CrossRef
25.Zhao, S., Yao, H., Yang, Y., Zhang, Y.: Affective image retrieval via multi-graph learning. In: ACM International Conference on Multimedia, pp. 1025–1028 (2014)
26.Zhao, S., Yao, H., Zhang, Y., Wang, Y., Liu, S.: View-based 3D object retrieval via multi-modal graph learning. Sig. Process. 112, 110–118 (2015)CrossRef - 作者单位：Dong Wang (19) (20)
Bin Wang (19)
Sicheng Zhao (20)
Hongxun Yao (20)
Hong Liu (19)
19. State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China
20. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China - 丛书名：MultiMedia Modeling
- ISBN：978-3-319-27671-7
- 刊物类别：Computer Science
- 刊物主题：Artificial Intelligence and Robotics
Computer Communication Networks
Software Engineering
Data Encryption
Database Management
Computation by Abstract Devices
Algorithm Analysis and Problem Complexity - 出版者：Springer Berlin / Heidelberg
- ISSN：1611-3349
文摘
View-based 3D object retrieval techniques have become prevalent in various fields, and lots of ingenious studies have promoted the development of retrieval performance from different aspects. In this paper, we focus on the 2D projective views that represent the 3D objects and propose a boosting approach by evaluating the discriminative ability of each object’s views. Different from previous works on selecting representative views of query object, we investigate the discriminative information of each view in dataset. By employing the proposed reverse distance metric, we utilize the discriminative information for many to many view set matching. The proposed algorithm is then employed with various features to boost the multi-model graph learning method. We compare our approach with several state of the art methods on ETH-80 dataset and National Taiwan University 3D model dataset. The results demonstrate the effectiveness of our method and its excellent boosting performance.