Unsupervised Distance Learning By Reciprocal Knn Distance For Image Retrieval

dc.creatorPedronette D.C.G.
dc.creatorPenatti O.A.B.
dc.creatorCalumby R.T.
dc.creatorDa S. Torres R.
dc.date2014
dc.date2015-06-25T18:00:04Z
dc.date2015-11-26T14:59:33Z
dc.date2015-06-25T18:00:04Z
dc.date2015-11-26T14:59:33Z
dc.date.accessioned2018-03-28T22:11:06Z
dc.date.available2018-03-28T22:11:06Z
dc.identifier
dc.identifierIcmr 2014 - Proceedings Of The Acm International Conference On Multimedia Retrieval 2014. Association For Computing Machinery, v. , n. , p. 345 - 352, 2014.
dc.identifier
dc.identifier10.1145/2578726.2578770
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84899769548&partnerID=40&md5=25c4854cdf0578d383c54490f03dbe7a
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/87387
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/87387
dc.identifier2-s2.0-84899769548
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1256070
dc.descriptionThis paper presents a novel unsupervised learning approach that takes into account the intrinsic dataset structure, which is represented in terms of the reciprocal neighborhood references found in different ranked lists. The proposed Reciprocal kNN Distance defines a more effective distance between two images, and is used to improve the effectiveness of image retrieval systems. Several experiments were conducted for different image retrieval tasks involving shape, color, and texture descriptors. The proposed approach is also evaluated on multimodal retrieval tasks, considering visual and textual descriptors. Experimental results demonstrate the effectiveness of proposed approach. The Reciprocal kNN Distance yields better results in terms of effectiveness than various state-of-the-art algorithms. Copyright © 2014 ACM.
dc.description
dc.description
dc.description345
dc.description352
dc.descriptionAMD; Advanced Micro Devices; Advanced Micro Devices
dc.descriptionArica, N., Vural, F.T.Y., BAS: A perceptual shape descriptor based on the beam angle statistics (2003) Pattern Recognition Letters, 24 (9-10), pp. 1627-1639
dc.descriptionBaeza-Yates, R.A., Ribeiro-Neto, B., (1999) Modern Information Retrieval, , Addison-Wesley Longman
dc.descriptionBai, X., Wang, B., Wang, X., Liu, W., Tu, Z., Co-transduction for shape retrieval (2010) ECCV, 3, pp. 328-341
dc.descriptionBelongie, S., Malik, J., Puzicha, J., Shape matching and object recognition using shape contexts (2002) PAMI, 24 (4), pp. 509-522
dc.descriptionBrodatz, P., (1966) Textures: A Photographic Album for Artists and Designers, , Dover
dc.descriptionChen, Y., Li, X., Dick, A., Hill, R., Ranking consistency for image matching and object retrieval (2014) Pattern Recognition, 47 (3), pp. 1349-1360
dc.descriptionCormack, G.V., Clarke, C.L.A., Buettcher, S., Reciprocal rank fusion outperforms condorcet and individual rank learning methods (2009) ACM SIGIR, pp. 758-759
dc.descriptionDa Torres, R.S., Falcão, A.X., Content-based image retrieval: Theory and applications (2006) Revista de Informática Teórica e Aplicada, 13 (2), pp. 161-185
dc.descriptionDa Torres, R.S., Falcão, A.X., Contour salience descriptors for effective image retrieval and analysis (2007) Image and Vision Computing, 25 (1), pp. 3-13
dc.descriptionDatta, R., Joshi, D., Li, J., Wang, J.Z., Image retrieval: Ideas, influences, and trends of the new age (2008) ACM Computing Surveys, 40 (2), pp. 501-560
dc.descriptionDeselaers, T., Keysers, D., Ney, H., Features for image retrieval: An experimental comparison (2008) Information Retrieval, 11 (2), pp. 77-107
dc.descriptionGhahramani, Z., Unsupervised learning (2004) Advanced Lectures on Machine Learning, pp. 72-112
dc.descriptionGopalan, R., Turaga, P., Chellappa, R., Articulation-invariant representation of non-planar shapes (2010) ECCV?2010, 3, pp. 286-299
dc.descriptionHuang, C.-B., Liu, Q., An orientation independent texture descriptor for image retrieval (2007) ICCCAS 2007, pp. 772-776
dc.descriptionHuang, J., Kumar, S.R., Mitra, M., Zhu, W.-J., Zabih, R., Image indexing using color correlograms (1997) CVPR, pp. 762-768
dc.descriptionJegou, H., Schmid, C., Harzallah, H., Verbeek, J., Accurate image search using the contextual dissimilarity measure (2010) PAMI, 32 (1), pp. 2-11
dc.descriptionJiang, J., Wang, B., Tu, Z., Unsupervised metric learning by self-smoothing operator (2011) ICCV, pp. 794-801
dc.descriptionKontschieder, P., Donoser, M., Bischof, H., Beyond pairwise shape similarity analysis (2009) ACCV, pp. 655-666
dc.descriptionKovalev, V., Volmer, S., Color co-occurence descriptors for querying-by-example (1998) International Conference on Multimedia Modeling, p. 32
dc.descriptionLafon, S., Lee, A.B., Diffusion maps and coarse-graining: A unified framework for dimensionality reduction, graph partitioning, and data set parameterization (2006) PAMI, 28 (9), pp. 1393-1403
dc.descriptionLatecki, L.J., Lakmper, R., Eckhardt, U., Shape descriptors for non-rigid shapes with a single closed contour (2000) CVPR, pp. 424-429
dc.descriptionLeibe, B., Schiele, B., Analyzing appearance and contour based methods for object categorization (2003) CVPR, 2. , II-409-15 vol.2
dc.descriptionLewis, J., Ossowski, S., Hicks, J., Errami, M., Garner, H.R., Text similarity: An alternative way to search medline (2006) Bioinformatics, 22 (18), pp. 2298-2304
dc.descriptionLing, H., Jacobs, D.W., Shape classification using the inner-distance (2007) PAMI, 29 (2), pp. 286-299
dc.descriptionLing, H., Yang, X., Latecki, L.J., Balancing deformability and discriminability for shape matching (2010) ECCV, 3, pp. 411-424
dc.descriptionLiu, Y., Zhang, D., Lu, G., Ma, W.-Y., A survey of content-based image retrieval with high-level semantics (2007) Pattern Recognition, 40 (1), pp. 262-282
dc.descriptionManjunath, B., Ohm, J.-R., Vasudevan, V., Yamada, A., Color and texture descriptors (2001) IEEE Transactions on Circuits and Systems for Video Technology, 11 (6), pp. 703-715
dc.descriptionOjala, T., Pietikäinen, M., Mäenpää, T., Multiresolution gray-scale and rotation invariant texture classification with local binary patterns (2002) PAMI, 24 (7), pp. 971-987
dc.descriptionPedronette, D.C.G., Da Torres, R.S., Shape retrieval using contour features and distance optmization (2010) VISAPP, 1, pp. 197-202
dc.descriptionPedronette, D.C.G., Da Torres, R.S., Exploiting clustering approaches for image re-ranking (2011) Journal of Visual Languages and Computing, 22 (6), pp. 453-466
dc.descriptionPedronette, D.C.G., Da Torres, R.S., Exploiting pairwise recommendation and clustering strategies for image re-ranking (2012) Information Sciences, 207, pp. 19-34
dc.descriptionPedronette, D.C.G., Da Torres, R.S., Image re-ranking and rank aggregation based on similarity of ranked lists (2013) Pattern Recognition, 46 (8), pp. 2350-2360
dc.descriptionQin, D., Gammeter, S., Bossard, L., Quack, T., Van Gool, L., Hello neighbor: Accurate object retrieval with k-reciprocal nearest neighbors (2011) CVPR, pp. 777-784. , june
dc.descriptionQin, T., Liu, T.-Y., Zhang, X.-D., Wang, D.-S., Li, H., Global ranking using continuous conditional random fields (2008) NIPS, pp. 1281-1288
dc.descriptionRobertson, S.E., Walker, S., Jones, S., Hancock-Beaulieu, M., Gatford, M., Okapi at trec-3 (1994) TREC, pp. 109-126
dc.descriptionStehling, R.O., Nascimento, M.A., Falcão, A.X., A compact and efficient image retrieval approach based on border/interior pixel classification (2002) CIKM, pp. 102-109
dc.descriptionSwain, M.J., Ballard, D.H., Color indexing (1991) International Journal on Computer Vision, 7 (1), pp. 11-32
dc.descriptionTao, B., Dickinson, B.W., Texture recognition and image retrieval using gradient indexing (2000) JVCIR, 11 (3), pp. 327-342
dc.descriptionTenenbaum, J.B., Silva, V.D., Langford, J.C., A global geometric framework for nonlinear dimensionality reduction (2000) Science, 290 (5500), pp. 2319-2323
dc.descriptionTu, Z., Yuille, A.L., Shape matching and recognition - Using generative models and informative features (2004) ECCV, pp. 195-209
dc.descriptionVan De Weijer, J., Schmid, C., Coloring local feature extraction (2006) ECCV, pp. 334-348
dc.descriptionVan Rijsbergen, C.J., (1979) Information Retrieval, , Butterworth-Heinemann, London
dc.descriptionWang, B., Jiang, J., Wang, W., Zhou, Z.-H., Tu, Z., Unsupervised metric fusion by cross diffusion (2012) CVPR, pp. 3013-3020
dc.descriptionWilliams, A., Yoon, P., Content-based image retrieval using joint correlograms (2007) MTAP, 34 (2), pp. 239-248. , August
dc.descriptionWolf, L., Hassner, T., Taigman, Y., Similarity scores based on background samples (2009) ACCV?09, pp. 88-97
dc.descriptionWu, P., Manjunanth, B.S., Newsam, S.D., Shin, H.D., A texture descriptor for image retrieval and browsing (1999) CBAIVL, pp. 3-7
dc.descriptionYang, X., Bai, X., Latecki, L.J., Tu, Z., Improving shape retrieval by learning graph transduction (2008) ECCV, 4, pp. 788-801
dc.descriptionYang, X., Koknar-Tezel, S., Latecki, L.J., Locally constrained diffusion process on locally densified distance spaces with applications to shape retrieval (2009) CVPR, pp. 357-364
dc.descriptionYang, X., Prasad, L., Latecki, L., Affinity learning with diffusion on tensor product graph (2012) PAMI, PP (99), p. 1
dc.descriptionYoung, H.P., An axiomatization of borda?s rule (1974) Journal of Economic Theory, 9 (1), pp. 43-52
dc.descriptionZhang, S., Yang, M., Cour, T., Yu, K., Metaxas, D.N., Query specific fusion for image retrieval ECCV, 2012, pp. 660-673
dc.languageen
dc.publisherAssociation for Computing Machinery
dc.relationICMR 2014 - Proceedings of the ACM International Conference on Multimedia Retrieval 2014
dc.rightsfechado
dc.sourceScopus
dc.titleUnsupervised Distance Learning By Reciprocal Knn Distance For Image Retrieval
dc.typeActas de congresos


Este ítem pertenece a la siguiente institución