Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Multimedia Systems
Published in: Multimedia Systems 5/2022

04-05-2022 | Regular Paper

Feature representation for 3D object retrieval based on unconstrained multi-view

Authors: Bin Zhou, Xuanyin Wang

Published in: Multimedia Systems | Issue 5/2022

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Reasonable and accurate image feature representation is the key to successful object retrieval. In this paper, we propose a 3D object feature representation method based on multiple views rather than a shape model. Unlike existing view-based methods that use pre-designed camera arrays to capture views, our method is flexible to implement by using several unconstrained views. Firstly, we generate a histogram of word frequencies to represent each view through local feature quantization. Then we integrate the histogram vectors of views belonging to the same object to generate a complete feature representation. Finally, similarity between two features is calculated for object retrieval. Several criteria are employed to evaluate the retrieval quality of the proposed method. Experimental results show that the integrated model feature is more effective and efficient than a set of individual image features and our approach is also competitive among several state-of-the-art methods.
previous article Visual saliency detection via combining center prior and U-Net
next article CED-Net: contextual encoder–decoder network for 3D face reconstruction

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
Literature
1.
Liu, Y., Zhang, D., Lu, G., et al.: A survey of content-based image retrieval with high-level semantics. Pattern Recogn. 40(1), 262–282 (2007)CrossRef
2.
Gao, Y., Dai, Q.H.: View-based 3D object retrieval: challenges and approaches. IEEE Multimedia 21(3), 52–57 (2014)CrossRef
3.
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 2008, Proceedings, pp. 93–102 (2008)
4.
Chen, X., Li, J., Shi, Z., et al.: Distinctive local surface descriptor for three-dimensional objects based on bispectrum of spherical harmonics. J. Electron. Imaging 25(1), 013021 (2016)CrossRef
5.
Tabia, H., Colot, O., Daoudi, M., et al.: Three-dimensional object retrieval based on vector quantization of invariant descriptors. J. Electron. Imaging 21(2), 023011 (2012)CrossRef
6.
Wang, P.S., et al.: O-CNN: octree-based convolutional neural networks for 3D shape analysis. ACM Trans. Graphics 36(4), 72 (2017)CrossRef
7.
Qi, R.C., Su, H., Niebner, M., Dai, A., Yan, M., Guibas, L.J.: Volumetric and multi-view CNNs for object classification on 3D data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5648–5656 (2016)
8.
Bai, S., Bai, X., Zhou, Z., Zhang, Z., Latecki, L.J.: GIFT: a real-time and scalable 3D shape search engine. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
9.
Gao, Y., Wang, M., Ji, R.R., et al.: 3-D object retrieval with Hausdorff distance learning. IEEE Trans. Industr. Electron. 61(4), 2088–2098 (2014)CrossRef
10.
Gao, Y., Dai, Q.H., Wang, M., et al.: 3D model retrieval using weighted bipartite graph matching. Signal Process.-Image Commun. 26(1), 39–47 (2011)CrossRef
11.
Gao, Y., Wang, M., Tao, D.C., et al.: 3-D object retrieval and recognition with Hypergraph analysis. IEEE Trans. Image Process. 21(9), 4290–4303 (2012)MathSciNetCrossRef
12.
Wang, M., Gao, Y., Lu, K., et al.: View-based discriminative probabilistic modeling for 3D object retrieval and recognition. IEEE Trans. Image Process. 22(4), 1395–1407 (2013)MathSciNetCrossRef
13.
Zhao, S., Yao, H., Zhang, Y., et al.: View-based 3D object retrieval via multi-modal graph learning. Signal Process. 112, 110–118 (2015)CrossRef
14.
Liu, A., Wang, Z.Y., Nie, W.Z., et al.: Graph-based characteristic view set extraction and matching for 3D model retrieval. Inf. Sci. 320, 429–442 (2015)CrossRef
15.
Chen, D.Y., Tian, X.P., Shen, Y.T., et al.: On visual similarity based 3D model retrieval. Comput. Graph. Forum 22(3), 223–232 (2003)CrossRef
16.
Daras, P., Axenopoulos, A.: A 3D shape retrieval framework supporting multimodal queries. Int. J. Comput. Vis. 89(2–3), 229–247 (2010)CrossRef
17.
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
18.
Gao, Y., Tang, J.H., Hong, R.C., et al.: Camera constraint-free view-based 3-D object retrieval. IEEE Trans. Image Process. 21(4), 2269–2281 (2012)MathSciNetCrossRef
19.
Mahmoudi, S., Daoudi, M.: 3D models retrieval by using characteristic views. In: 16th International Conference on Pattern Recognition, Vol Ii, Proceedings, pp. 457–460 (2002)
20.
Gao, Y., Dai, Q.H., Zhang, N.Y.: 3D model comparison using spatial structure circular descriptor. Pattern Recogn. 43(3), 1142–1151 (2010)CrossRef
21.
Papadakis, P., Pratikakis, I., Theoharis, T., et al.: PANORAMA: a 3D shape descriptor based on panoramic views for unsupervised 3D object retrieval. Int. J. Comput. Vis. 89(2–3), 177–192 (2010)CrossRef
22.
Kim, W.Y., Kim, Y.S.: A region-based shape descriptor using Zernike moments. Signal Process.-Image Commun. 16(1–2), 95–102 (2000)CrossRef
23.
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRef
24.
Gao, Z., Li, Y., Wan, S.: Exploring deep learning for view-based 3D model retrieval. ACM Trans. Multimed. Comput. Commun. Appl. (TOMM) 16(1), 1–21 (2020)CrossRef
25.
Gao, Z., Xue, K.X., Wan, S.H.: Multiple discrimination and pairwise CNN for view-based 3D object retrieval. Neural Netw. 125, 290–302 (2020)CrossRef
26.
Gao, Z., et al.: Adaptive fusion and category-level dictionary learning model for multiview human action recognition. IEEE Internet Things J. 6(6), 9280–9293 (2019)CrossRef
27.
Li, F., Perona, P.: A Bayesian hierarchical model for learning natural scene categories. In: Proceeding of IEEE Computer Vision and Pattern Recognition. pp. 524–531 (2005)
28.
Passalis, N., Tefas, A.: Entropy optimized feature-based bag-of-words representation for information retrieval[J]. IEEE Trans. Knowl. Data Eng. 28(7), 1664–1677 (2016)CrossRef
29.
Ergun, H., Sert, M.: Efficient bag of words based concept extraction for visual object retrieval. Springer International Publishing (2016)
30.
Lavoue, G.: Combination of bag-of-words descriptors for robust partial shape retrieval[J]. Vis. Comput. 28(9), 931–942 (2012)CrossRef
31.
Toldo, R., Castellani, U., Fusiello, A.: The bag of words approach for retrieval and categorization of 3D objects. Vis. Comput. 26(10), 1257–1268 (2010)CrossRef
32.
Sedmidubsky. J., Budikova, P., Dohnal, V., Zezula, P.: Motion words: a text-like representation of 3D skeleton sequences. In: 42nd European Conference on Information Retrieval (ECIR) (2020)
33.
Budikova, P., et al.: Efficient Indexing of 3D Human Motions. In: ACM International Conference on Multimedia Retrieval (ICMR), pp. 10–18 (2021)
34.
Duda, O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley & Sons, Hoboken (2012)MATH
35.
Van Gemert, J.C., et al.: Visual word ambiguity. IEEE Trans. Pattern Anal. Mach. Intell. 32(7), 1271–1283 (2009)CrossRef
36.
Leibe, B., Schiele, B.: Analyzing appearance and contour based methods for object categorization. In: 2003 IEEE Computer Society Conference on Computer Vision And Pattern Recognition, Vol Ii, Proceedings, pp. 409–415 (2003)
Metadata
Title
Feature representation for 3D object retrieval based on unconstrained multi-view
Authors
Bin Zhou
Xuanyin Wang
Publication date
04-05-2022
Publisher
Springer Berlin Heidelberg
Published in
Multimedia Systems / Issue 5/2022
Print ISSN: 0942-4962
Electronic ISSN: 1432-1882
DOI
https://doi.org/10.1007/s00530-022-00939-1

Other articles of this Issue 5/2022

Multimedia Systems 5/2022 Go to the issue

Regular Paper

Overcoming the practical restrictions in H.266/VVC-based video communication systems by a PI bit rate controller

Regular Paper

Optimized generative adversarial network based breast cancer diagnosis with wavelet and texture features

Regular Article

A convolutional neural network and classical moments-based feature fusion model for gesture recognition

Regular Paper

Improving CT-image universal lesion detection with comprehensive data and feature enhancements

Regular Paper

CED-Net: contextual encoder–decoder network for 3D face reconstruction

Regular Paper

Multiview clustering via consistent and specific nonnegative matrix factorization with graph regularization