Supervised learning of bag-of-features shape descriptors using sparse coding
We present a method for supervised learning of shape descriptors for shape retrieval applications. Many content‐based shape retrieval approaches follow the bag‐of‐features (BoF) paradigm commonly used in text and image retrieval by first computing local shape descriptors, and then representing them...
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| Published in: | Computer graphics forum Vol. 33; no. 5; pp. 127 - 136 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Oxford
Blackwell Publishing Ltd
01.08.2014
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| ISSN: | 0167-7055, 1467-8659 |
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| Abstract | We present a method for supervised learning of shape descriptors for shape retrieval applications. Many content‐based shape retrieval approaches follow the bag‐of‐features (BoF) paradigm commonly used in text and image retrieval by first computing local shape descriptors, and then representing them in a ‘geometric dictionary’ using vector quantization. A major drawback of such approaches is that the dictionary is constructed in an unsupervised manner using clustering, unaware of the last stage of the process (pooling of the local descriptors into a BoF, and comparison of the latter using some metric). In this paper, we replace the clustering with dictionary learning, where every atom acts as a feature, followed by sparse coding and pooling to get the final BoF descriptor. Both the dictionary and the sparse codes can be learned in the supervised regime via bi‐level optimization using a task‐specific objective that promotes invariance desired in the specific application. We show significant performance improvement on several standard shape retrieval benchmarks. |
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| AbstractList | We present a method for supervised learning of shape descriptors for shape retrieval applications. Many content‐based shape retrieval approaches follow the bag‐of‐features (BoF) paradigm commonly used in text and image retrieval by first computing local shape descriptors, and then representing them in a ‘geometric dictionary’ using vector quantization. A major drawback of such approaches is that the dictionary is constructed in an unsupervised manner using clustering, unaware of the last stage of the process (pooling of the local descriptors into a BoF, and comparison of the latter using some metric). In this paper, we replace the clustering with dictionary learning, where every atom acts as a feature, followed by sparse coding and pooling to get the final BoF descriptor. Both the dictionary and the sparse codes can be learned in the supervised regime via bi‐level optimization using a task‐specific objective that promotes invariance desired in the specific application. We show significant performance improvement on several standard shape retrieval benchmarks. We present a method for supervised learning of shape descriptors for shape retrieval applications. Many content-based shape retrieval approaches follow the bag-of-features (BoF) paradigm commonly used in text and image retrieval by first computing local shape descriptors, and then representing them in a 'geometric dictionary' using vector quantization. A major drawback of such approaches is that the dictionary is constructed in an unsupervised manner using clustering, unaware of the last stage of the process (pooling of the local descriptors into a BoF, and comparison of the latter using some metric). In this paper, we replace the clustering with dictionary learning, where every atom acts as a feature, followed by sparse coding and pooling to get the final BoF descriptor. Both the dictionary and the sparse codes can be learned in the supervised regime via bi-level optimization using a task-specific objective that promotes invariance desired in the specific application. We show significant performance improvement on several standard shape retrieval benchmarks. [PUBLICATION ABSTRACT] |
| Author | Bronstein, Alex Castellani, Umberto Bronstein, Michael Litman, Roee |
| Author_xml | – sequence: 1 givenname: Roee surname: Litman fullname: Litman, Roee organization: School of Electrical Engineering, Tel-Aviv University – sequence: 2 givenname: Alex surname: Bronstein fullname: Bronstein, Alex organization: School of Electrical Engineering, Tel-Aviv University – sequence: 3 givenname: Michael surname: Bronstein fullname: Bronstein, Michael organization: Faculty of Informatics, University of Lugano – sequence: 4 givenname: Umberto surname: Castellani fullname: Castellani, Umberto organization: Department of Computer Science, University of Verona |
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| Copyright | 2014 The Author(s) Computer Graphics Forum © 2014 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd. 2014 The Eurographics Association and John Wiley & Sons Ltd. |
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| References | Lavoue G.: Combination of bag-of-words descriptors for robust partial shape retrieval. The Visual Computer 26 (2012), 1257-1268. 1 Li C., Hamza A.: Intrinsic spatial pyramid matching for deformable 3d shape retrieval. J. Multimedia Information Retrieval 2, 4 (2013), 261-271. 1, 7, 8 Aharon M., Elad M., Bruckstein A.: k-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. Trans. Signal Processing 54, 11 (2006), 4311-4322. 2, 4 Hofmann T.: Unsupervised learning by probabilistic latent semantic analysis. Mach. Learn. 42, 1-2 (2001), 177-196. 2 Ovsjanikov M., Li W., Guibas L., Mitra N.J.: Exploration of continuous variability in collections of 3D shapes. TOG 30, 4 (2011), 33. 1 Engan K., Aase S.O., Hakon Husoy J.: Method of optimal directions for frame design. In Proc. ICASSP (1999), vol. 5. 4 Lian Z., et al.: A Comparison of Methods for Non-rigid 3D Shape Retrieval. Pattern Recognition 46, 1 (2013), 449-461. 1 Liu Y., Wang X.-L., Wang H.-Y., Zha H., Qin H.: Learning robust similarity measures for 3d partial shape retrieval. International Journal of Computer Vision 89, 2-3 (2010), 408-431. 1 Bronstein A.M., Bronstein M.M., Guibas L.J., Ovsjanikov M.: Shape google: Geometric words and expressions for invariant shape retrieval. TOG 30, 1 (2011), 1-20. 1, 2, 7, 8 López-Sastre R.J., García-Fuertes A., Redondo-Cabrera C., Acevedo-Rodríguez F.J., Maldonado-Bascón S.: Evaluating 3d spatial pyramids for classifying 3d shapes. Computers and Graphics 37, 5 (2013), 473-483. 1 Funkhouser T., Kazhdan M., Min P., Shilane P.: Shape-based retrieval and analysis of 3D models. Comm. ACM 48 (2005), 58-64. 1 Pinkall U., Polthier K.: Computing discrete minimal surfaces and their conjugates. Experimental mathematics 2, 1 (1993), 15-36. 2 GONG B., LIU J., Wang X., TANG X.: Learning semantic signatures for 3d object retrieval. 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| References_xml | – reference: Hu R., Fan L., Liu L.: Co-segmentation of 3d shapes via subspace clustering. Computer Graphics Forum 31, 5 (2012), 1703-1713. 2 – reference: Li C., Hamza A.B.: A multiresolution descriptor for deformable 3d shape retrieval. The Visual Computer 29, 6-8 (2013), 513-524. 7, 8 – reference: Aharon M., Elad M., Bruckstein A.: k-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. Trans. Signal Processing 54, 11 (2006), 4311-4322. 2, 4 – reference: Darom T., Keller Y.: Scale-Invariant Features for 3-D Mesh Models. Trans. Image Processing 21, 5 (2012), 2758-2769. 1 – reference: Colson B., Marcotte P., Savard G.: An overview of bilevel optimization. Ann. Oper. Res. 153, 1 (2007), 235-256. 5 – reference: Funkhouser T., Kazhdan M., Min P., Shilane P.: Shape-based retrieval and analysis of 3D models. Comm. ACM 48 (2005), 58-64. 1 – reference: Duda R., Hart P., Stork D.: Pattern Classification, second ed. John Wiley & Sons, 2001. 2 – reference: Lavoue G.: Combination of bag-of-words descriptors for robust partial shape retrieval. The Visual Computer 26 (2012), 1257-1268. 1 – reference: Toldo R., Castellani U., Fusiello A.: The bag of words approach for retrieval and categorization of 3D objects. The Visual Computer 26 (2010), 1257-1268. 1 – reference: Liu Y., Wang X.-L., Wang H.-Y., Zha H., Qin H.: Learning robust similarity measures for 3d partial shape retrieval. International Journal of Computer Vision 89, 2-3 (2010), 408-431. 1 – reference: Sun J., Ovsjanikov M., Guibas L.: A Concise and Provably Informative Multi-Scale Signature Based on Heat Diffusion. vol. 28, pp. 1383-1392. 1, 2, 7 – reference: Pinkall U., Polthier K.: Computing discrete minimal surfaces and their conjugates. Experimental mathematics 2, 1 (1993), 15-36. 2 – reference: GONG B., LIU J., Wang X., TANG X.: Learning semantic signatures for 3d object retrieval. Trans. on Multimedia 15, 2 (2013), 369-377. 1 – reference: Lian Z., et al.: A Comparison of Methods for Non-rigid 3D Shape Retrieval. Pattern Recognition 46, 1 (2013), 449-461. 1 – reference: Lazebnik S., Raginsky M.: Supervised learning of quantizer codebooks by information loss minimization. Trans. PAMI 31, 7 (July 2009), 1294-1309. 2 – reference: Blei D.M., Ng A.Y., Jordan M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3 (Mar. 2003), 993-1022. 2 – reference: Huang Q.-X., Su H., Guibas L.: Fine-grained semi-supervised labeling of large shape collections. TOG 32, 6 (2013), 190. 1, 8 – reference: Bronstein A.M., Bronstein M.M., Guibas L.J., Ovsjanikov M.: Shape google: Geometric words and expressions for invariant shape retrieval. TOG 30, 1 (2011), 1-20. 1, 2, 7, 8 – reference: Giachetti A., Lovato C.: Radial symmetry detection and shape characterization with the multiscale area projection transform. Computer Graphics Forum 31, 5 (2012), 1669-1678. 7, 8 – reference: Engan K., Aase S.O., Hakon Husoy J.: Method of optimal directions for frame design. In Proc. ICASSP (1999), vol. 5. 4 – reference: Weinberger K.Q., Saul L.K.: Distance metric learning for large margin nearest neighbor classification. JMLR 10 (2009), 207-244. 4 – reference: López-Sastre R.J., García-Fuertes A., Redondo-Cabrera C., Acevedo-Rodríguez F.J., Maldonado-Bascón S.: Evaluating 3d spatial pyramids for classifying 3d shapes. Computers and Graphics 37, 5 (2013), 473-483. 1 – reference: Ovsjanikov M., Li W., Guibas L., Mitra N.J.: Exploration of continuous variability in collections of 3D shapes. TOG 30, 4 (2011), 33. 1 – reference: Akgül C.B., Sankur B., Yemez Y., Schmitt F.: Similarity learning for 3d object retrieval using relevance feedback and risk minimization. IJCV 89, 2-3 (2010), 392-407. 1 – reference: Vapnik V.: Statistical Learning Theory. 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| SubjectTerms | Analysis Artificial intelligence Clustering Coding Computer graphics Computer programming Dictionaries Image retrieval Learning Optimization Retrieval Studies Texts Vector quantization |
| Title | Supervised learning of bag-of-features shape descriptors using sparse coding |
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