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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| Vydáno v: | Computer graphics forum Ročník 33; číslo 5; s. 127 - 136 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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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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| Cites_doi | 10.1007/s10479-007-0176-2 10.1162/jmlr.2003.3.4-5.993 10.1080/10586458.1993.10504266 10.1007/s11263-009-0298-x 10.1145/1553374.1553463 10.1016/j.cag.2013.04.003 10.1109/TIP.2012.2183142 10.1007/s00138-013-0501-5 10.1155/ASP/2006/52561 10.1109/TPAMI.2011.156 10.1145/2461466.2461488 10.1007/s00371-013-0815-3 10.1109/CVPR.2008.4587652 10.1007/s00371-010-0519-x 10.1109/ICCV.2003.1238663 10.1145/1064830.1064859 10.1145/2508363.2508364 10.1145/1899404.1899405 10.1109/TPAMI.2008.138 10.1007/s13735-013-0041-9 10.1016/j.patcog.2012.07.014 10.1023/A:1007617005950 10.1109/ICCV.2013.215 10.1002/cpa.20042 10.1007/s11263-009-0294-1 10.1109/CVPR.2010.5539838 10.1109/ICCV.2005.239 10.1109/ICCV.2013.154 10.1111/j.1467-8659.2012.03175.x 10.1109/TMM.2012.2231059 10.1145/2010324.1964928 10.1111/j.1467-8659.2012.03172.x 10.1109/TSP.2006.881199 |
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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. Trans. on Multimedia 15, 2 (2013), 369-377. 1 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 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 Weinberger K.Q., Saul L.K.: Distance metric learning for large margin nearest neighbor classification. JMLR 10 (2009), 207-244. 4 Blei D.M., Ng A.Y., Jordan M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3 (Mar. 2003), 993-1022. 2 Mairal J., Bach F., Ponce J.: Task-driven dictionary learning. Trans. PAMI 34, 4 (2012), 791-804. 2, 5, 6 Colson B., Marcotte P., Savard G.: An overview of bilevel optimization. Ann. Oper. Res. 153, 1 (2007), 235-256. 5 Daubechies I., Defrise M., De Mol C.: An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Comm. Pure and Applied Mathematics 57, 11 (2004), 1413-1457. 9 Li C., Hamza A.B.: A multiresolution descriptor for deformable 3d shape retrieval. The Visual Computer 29, 6-8 (2013), 513-524. 7, 8 Vapnik V.: Statistical Learning Theory. Wiley, New York, 1998. 2 Lian Z., Godil A., Sun X., Xiao J.: CM-BOF: visual similarity-based 3D shape retrieval using clock matching and bag-of-features. Machine Vision and Applications 24, 8 (2013), 1685-1704. 1 Lazebnik S., Raginsky M.: Supervised learning of quantizer codebooks by information loss minimization. Trans. PAMI 31, 7 (July 2009), 1294-1309. 2 Hu R., Fan L., Liu L.: Co-segmentation of 3d shapes via subspace clustering. Computer Graphics Forum 31, 5 (2012), 1703-1713. 2 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 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 Duda R., Hart P., Stork D.: Pattern Classification, second ed. John Wiley & Sons, 2001. 2 Huang Q.-X., Su H., Guibas L.: Fine-grained semi-supervised labeling of large shape collections. TOG 32, 6 (2013), 190. 1, 8 Darom T., Keller Y.: Scale-Invariant Features for 3-D Mesh Models. Trans. Image Processing 21, 5 (2012), 2758-2769. 1 2013; 29 28 2013; 2 2012 2006; 54 2010 2013; 46 2013; 24 2009 1998 2011; 30 2008 2006 2005 2004 2003 2005; 48 2012; 34 1999; 5 1993; 2 2012; 31 2001; 42 2010; 89 2013; 15 2013; 37 2010; 26 2009; 10 2009; 31 2001 2013; 32 2007; 153 2004; 57 2003; 3 2014 2013 2012; 26 2012; 21 e_1_2_8_28_2 e_1_2_8_45_2 e_1_2_8_26_2 e_1_2_8_47_2 e_1_2_8_9_2 Mitra N. (e_1_2_8_40_2) 2013 Sun J. (e_1_2_8_46_2); 28 e_1_2_8_3_2 e_1_2_8_5_2 e_1_2_8_7_2 e_1_2_8_20_2 e_1_2_8_41_2 e_1_2_8_22_2 e_1_2_8_43_2 e_1_2_8_17_2 e_1_2_8_38_2 e_1_2_8_19_2 e_1_2_8_34_2 Weinberger K.Q. (e_1_2_8_50_2) 2009; 10 e_1_2_8_15_2 e_1_2_8_36_2 Engan K. (e_1_2_8_13_2) 1999; 5 e_1_2_8_30_2 e_1_2_8_32_2 e_1_2_8_51_2 e_1_2_8_27_2 e_1_2_8_29_2 e_1_2_8_23_2 e_1_2_8_25_2 e_1_2_8_48_2 Lavoue G. (e_1_2_8_24_2) 2012; 26 e_1_2_8_2_2 e_1_2_8_4_2 e_1_2_8_6_2 e_1_2_8_8_2 e_1_2_8_42_2 e_1_2_8_21_2 e_1_2_8_44_2 e_1_2_8_16_2 e_1_2_8_39_2 e_1_2_8_18_2 e_1_2_8_12_2 e_1_2_8_35_2 e_1_2_8_14_2 e_1_2_8_37_2 Vapnik V. (e_1_2_8_49_2) 1998 Duda R. (e_1_2_8_11_2) 2001 e_1_2_8_31_2 e_1_2_8_10_2 e_1_2_8_33_2 e_1_2_8_52_2 |
| 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. Wiley, New York, 1998. 2 – reference: 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 – reference: Mairal J., Bach F., Ponce J.: Task-driven dictionary learning. Trans. PAMI 34, 4 (2012), 791-804. 2, 5, 6 – reference: Daubechies I., Defrise M., De Mol C.: An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Comm. Pure and Applied Mathematics 57, 11 (2004), 1413-1457. 9 – reference: Hofmann T.: Unsupervised learning by probabilistic latent semantic analysis. Mach. Learn. 42, 1-2 (2001), 177-196. 2 – reference: Lian Z., Godil A., Sun X., Xiao J.: CM-BOF: visual similarity-based 3D shape retrieval using clock matching and bag-of-features. Machine Vision and Applications 24, 8 (2013), 1685-1704. 1 – volume: 21 start-page: 2758 issue: 5 year: 2012 end-page: 2769 article-title: Scale‐Invariant Features for 3-D Mesh Models publication-title: Trans. Image Processing – year: 2009 – volume: 29 start-page: 513 issue: 6 year: 2013 end-page: 8 524 article-title: A multiresolution descriptor for deformable 3d shape retrieval publication-title: The Visual Computer – volume: 30 start-page: 33 issue: 4 year: 2011 article-title: Exploration of continuous variability in collections of 3D shapes publication-title: TOG – year: 2005 – year: 2001 – year: 2003 – volume: 26 start-page: 1257 year: 2010 end-page: 1268 article-title: The bag of words approach for retrieval and categorization of 3D objects publication-title: The Visual Computer – volume: 89 start-page: 392 issue: 2 year: 2010 end-page: 3 407 article-title: Similarity learning for 3d object retrieval using relevance feedback and risk minimization publication-title: IJCV – volume: 54 start-page: 4311 issue: 11 year: 2006 end-page: 4322 article-title: k‐SVD: an algorithm for designing overcomplete dictionaries for sparse representation publication-title: Trans. Signal Processing – volume: 31 start-page: 1669 issue: 5 year: 2012 end-page: 1678 article-title: Radial symmetry detection and shape characterization with the multiscale area projection transform publication-title: Computer Graphics Forum – volume: 5 start-page: 4 year: 1999 article-title: Method of optimal directions for frame design publication-title: Proc. ICASSP – volume: 24 start-page: 1685 issue: 8 year: 2013 end-page: 1704 article-title: CM‐BOF: visual similarity‐based 3D shape retrieval using clock matching and bag‐of‐features publication-title: Machine Vision and Applications – volume: 57 start-page: 1413 issue: 11 year: 2004 end-page: 1457 article-title: An iterative thresholding algorithm for linear inverse problems with a sparsity constraint publication-title: Comm. Pure and Applied Mathematics – year: 2014 – volume: 46 start-page: 449 issue: 1 year: 2013 end-page: 461 article-title: A Comparison of Methods for Non‐rigid 3D Shape Retrieval publication-title: Pattern Recognition – volume: 89 start-page: 408 issue: 2 year: 2010 end-page: 3 431 article-title: Learning robust similarity measures for 3d partial shape retrieval publication-title: International Journal of Computer Vision – year: 2010 – volume: 42 start-page: 177 issue: 1 year: 2001 end-page: 2 196 article-title: Unsupervised learning by probabilistic latent semantic analysis publication-title: Mach. Learn – year: 1998 – year: 2012 – start-page: 102 year: 2006 end-page: 102 – start-page: 121 year: 2013 end-page: 126 – volume: 2 start-page: 15 issue: 1 year: 1993 end-page: 36 article-title: Computing discrete minimal surfaces and their conjugates publication-title: Experimental mathematics – volume: 28 start-page: 1383 end-page: 1392 publication-title: A Concise and Provably Informative Multi‐Scale Signature Based on Heat Diffusion – volume: 153 start-page: 235 issue: 1 year: 2007 end-page: 256 article-title: An overview of bilevel optimization publication-title: Ann. Oper. Res – volume: 31 start-page: 1294 issue: 7 year: 2009 end-page: 1309 article-title: Supervised learning of quantizer codebooks by information loss minimization publication-title: Trans. PAMI – volume: 30 start-page: 1 issue: 1 year: 2011 end-page: 20 article-title: Shape google: Geometric words and expressions for invariant shape retrieval publication-title: TOG – volume: 26 start-page: 1257 year: 2012 end-page: 1268 article-title: Combination of bag‐of‐words descriptors for robust partial shape retrieval publication-title: The Visual Computer – volume: 15 start-page: 369 issue: 2 year: 2013 end-page: 377 article-title: Learning semantic signatures for 3d object retrieval publication-title: Trans. on Multimedia – year: 2008 – volume: 48 start-page: 58 year: 2005 end-page: 64 article-title: Shape‐based retrieval and analysis of 3D models publication-title: Comm. ACM – year: 2006 – year: 2004 – volume: 31 start-page: 1703 issue: 5 year: 2012 end-page: 1713 article-title: Co‐segmentation of 3d shapes via subspace clustering publication-title: Computer Graphics Forum – volume: 32 start-page: 190 issue: 6 year: 2013 article-title: Fine‐grained semi‐supervised labeling of large shape collections publication-title: TOG – volume: 34 start-page: 791 issue: 4 year: 2012 end-page: 804 article-title: Task‐driven dictionary learning publication-title: Trans. PAMI – volume: 37 start-page: 473 issue: 5 year: 2013 end-page: 483 article-title: Evaluating 3d spatial pyramids for classifying 3d shapes publication-title: Computers and Graphics – volume: 3 start-page: 993 year: 2003 end-page: 1022 article-title: Latent dirichlet allocation publication-title: J. Mach. Learn. Res – volume: 2 start-page: 261 issue: 4 year: 2013 end-page: 271 article-title: Intrinsic spatial pyramid matching for deformable 3d shape retrieval publication-title: J. Multimedia Information Retrieval – volume: 10 start-page: 207 year: 2009 end-page: 244 article-title: Distance metric learning for large margin nearest neighbor classification publication-title: JMLR – year: 2013 – ident: e_1_2_8_9_2 doi: 10.1007/s10479-007-0176-2 – ident: e_1_2_8_7_2 doi: 10.1162/jmlr.2003.3.4-5.993 – ident: e_1_2_8_43_2 doi: 10.1080/10586458.1993.10504266 – volume-title: Statistical Learning Theory year: 1998 ident: e_1_2_8_49_2 – ident: e_1_2_8_35_2 – ident: e_1_2_8_8_2 – ident: e_1_2_8_34_2 doi: 10.1007/s11263-009-0298-x – ident: e_1_2_8_39_2 doi: 10.1145/1553374.1553463 – ident: e_1_2_8_30_2 – ident: e_1_2_8_32_2 doi: 10.1016/j.cag.2013.04.003 – ident: e_1_2_8_12_2 doi: 10.1109/TIP.2012.2183142 – ident: e_1_2_8_27_2 doi: 10.1007/s00138-013-0501-5 – ident: e_1_2_8_45_2 doi: 10.1155/ASP/2006/52561 – volume: 28 start-page: 1383 ident: e_1_2_8_46_2 publication-title: A Concise and Provably Informative Multi‐Scale Signature Based on Heat Diffusion – volume: 26 start-page: 1257 year: 2012 ident: e_1_2_8_24_2 article-title: Combination of bag‐of‐words descriptors for robust partial shape retrieval publication-title: The Visual Computer – ident: e_1_2_8_38_2 doi: 10.1109/TPAMI.2011.156 – ident: e_1_2_8_52_2 doi: 10.1145/2461466.2461488 – ident: e_1_2_8_17_2 – ident: e_1_2_8_25_2 – ident: e_1_2_8_29_2 doi: 10.1007/s00371-013-0815-3 – ident: e_1_2_8_37_2 doi: 10.1109/CVPR.2008.4587652 – ident: e_1_2_8_36_2 – ident: e_1_2_8_33_2 – ident: e_1_2_8_48_2 doi: 10.1007/s00371-010-0519-x – ident: e_1_2_8_44_2 – ident: e_1_2_8_47_2 doi: 10.1109/ICCV.2003.1238663 – volume: 5 start-page: 4 year: 1999 ident: e_1_2_8_13_2 article-title: Method of optimal directions for frame design publication-title: Proc. ICASSP – ident: e_1_2_8_14_2 doi: 10.1145/1064830.1064859 – ident: e_1_2_8_22_2 doi: 10.1145/2508363.2508364 – ident: e_1_2_8_4_2 doi: 10.1145/1899404.1899405 – ident: e_1_2_8_31_2 doi: 10.1109/TPAMI.2008.138 – ident: e_1_2_8_28_2 doi: 10.1007/s13735-013-0041-9 – ident: e_1_2_8_5_2 – ident: e_1_2_8_26_2 doi: 10.1016/j.patcog.2012.07.014 – ident: e_1_2_8_21_2 doi: 10.1023/A:1007617005950 – ident: e_1_2_8_23_2 – ident: e_1_2_8_16_2 doi: 10.1109/ICCV.2013.215 – ident: e_1_2_8_42_2 – ident: e_1_2_8_10_2 doi: 10.1002/cpa.20042 – ident: e_1_2_8_3_2 doi: 10.1007/s11263-009-0294-1 – ident: e_1_2_8_6_2 doi: 10.1109/CVPR.2010.5539838 – volume-title: Pattern Classification year: 2001 ident: e_1_2_8_11_2 – ident: e_1_2_8_15_2 doi: 10.1109/ICCV.2005.239 – volume: 10 start-page: 207 year: 2009 ident: e_1_2_8_50_2 article-title: Distance metric learning for large margin nearest neighbor classification publication-title: JMLR – ident: e_1_2_8_51_2 doi: 10.1109/ICCV.2013.154 – ident: e_1_2_8_20_2 doi: 10.1111/j.1467-8659.2012.03175.x – volume-title: Proc. SIGGRAPH Asia year: 2013 ident: e_1_2_8_40_2 – ident: e_1_2_8_19_2 doi: 10.1109/TMM.2012.2231059 – ident: e_1_2_8_41_2 doi: 10.1145/2010324.1964928 – ident: e_1_2_8_18_2 doi: 10.1111/j.1467-8659.2012.03172.x – ident: e_1_2_8_2_2 doi: 10.1109/TSP.2006.881199 |
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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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