This 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. 345352AMD; Advanced Micro Devices; Advanced Micro DevicesArica, N., Vural, F.T.Y., BAS: A perceptual shape descriptor based on the beam angle statistics (2003) Pattern Recognition Letters, 24 (9-10), pp. 1627-1639Baeza-Yates, R.A., Ribeiro-Neto, B., (1999) Modern Information Retrieval, , Addison-Wesley LongmanBai, X., Wang, B., Wang, X., Liu, W., Tu, Z., Co-transduction for shape retrieval (2010) ECCV, 3, pp. 328-341Belongie, S., Malik, J., Puzicha, J., Shape matching and object recognition using shape contexts (2002) PAMI, 24 (4), pp. 509-522Brodatz, P., (1966) Textures: A Photographic Album for Artists and Designers, , DoverChen, Y., Li, X., Dick, A., Hill, R., Ranking consistency for image matching and object retrieval (2014) Pattern Recognition, 47 (3), pp. 1349-1360Cormack, G.V., Clarke, C.L.A., Buettcher, S., Reciprocal rank fusion outperforms condorcet and individual rank learning methods (2009) ACM SIGIR, pp. 758-759Da Torres, R.S., Falcão, A.X., Content-based image retrieval: Theory and applications (2006) Revista de Informática Teórica e Aplicada, 13 (2), pp. 161-185Da Torres, R.S., Falcão, A.X., Contour salience descriptors for effective image retrieval and analysis (2007) Image and Vision Computing, 25 (1), pp. 3-13Datta, 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. 501-560Deselaers, T., Keysers, D., Ney, H., Features for image retrieval: An experimental comparison (2008) Information Retrieval, 11 (2), pp. 77-107Ghahramani, Z., Unsupervised learning (2004) Advanced Lectures on Machine Learning, pp. 72-112Gopalan, R., Turaga, P., Chellappa, R., Articulation-invariant representation of non-planar shapes (2010) ECCV?2010, 3, pp. 286-299Huang, C.-B., Liu, Q., An orientation independent texture descriptor for image retrieval (2007) ICCCAS 2007, pp. 772-776Huang, J., Kumar, S.R., Mitra, M., Zhu, W.-J., Zabih, R., Image indexing using color correlograms (1997) CVPR, pp. 762-768Jegou, H., Schmid, C., Harzallah, H., Verbeek, J., Accurate image search using the contextual dissimilarity measure (2010) PAMI, 32 (1), pp. 2-11Jiang, J., Wang, B., Tu, Z., Unsupervised metric learning by self-smoothing operator (2011) ICCV, pp. 794-801Kontschieder, P., Donoser, M., Bischof, H., Beyond pairwise shape similarity analysis (2009) ACCV, pp. 655-666Kovalev, V., Volmer, S., Color co-occurence descriptors for querying-by-example (1998) International Conference on Multimedia Modeling, p. 32Lafon, 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. 1393-1403Latecki, L.J., Lakmper, R., Eckhardt, U., Shape descriptors for non-rigid shapes with a single closed contour (2000) CVPR, pp. 424-429Leibe, B., Schiele, B., Analyzing appearance and contour based methods for object categorization (2003) CVPR, 2. , II-409-15 vol.2Lewis, J., Ossowski, S., Hicks, J., Errami, M., Garner, H.R., Text similarity: An alternative way to search medline (2006) Bioinformatics, 22 (18), pp. 2298-2304Ling, H., Jacobs, D.W., Shape classification using the inner-distance (2007) PAMI, 29 (2), pp. 286-299Ling, H., Yang, X., Latecki, L.J., Balancing deformability and discriminability for shape matching (2010) ECCV, 3, pp. 411-424Liu, 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. 262-282Manjunath, 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. 703-715Ojala, T., Pietikäinen, M., Mäenpää, T., Multiresolution gray-scale and rotation invariant texture classification with local binary patterns (2002) PAMI, 24 (7), pp. 971-987Pedronette, D.C.G., Da Torres, R.S., Shape retrieval using contour features and distance optmization (2010) VISAPP, 1, pp. 197-202Pedronette, D.C.G., Da Torres, R.S., Exploiting clustering approaches for image re-ranking (2011) Journal of Visual Languages and Computing, 22 (6), pp. 453-466Pedronette, D.C.G., Da Torres, R.S., Exploiting pairwise recommendation and clustering strategies for image re-ranking (2012) Information Sciences, 207, pp. 19-34Pedronette, 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. 2350-2360Qin, 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. , juneQin, T., Liu, T.-Y., Zhang, X.-D., Wang, D.-S., Li, H., Global ranking using continuous conditional random fields (2008) NIPS, pp. 1281-1288Robertson, S.E., Walker, S., Jones, S., Hancock-Beaulieu, M., Gatford, M., Okapi at trec-3 (1994) TREC, pp. 109-126Stehling, R.O., Nascimento, M.A., Falcão, A.X., A compact and efficient image retrieval approach based on border/interior pixel classification (2002) CIKM, pp. 102-109Swain, M.J., Ballard, D.H., Color indexing (1991) International Journal on Computer Vision, 7 (1), pp. 11-32Tao, B., Dickinson, B.W., Texture recognition and image retrieval using gradient indexing (2000) JVCIR, 11 (3), pp. 327-342Tenenbaum, J.B., Silva, V.D., Langford, J.C., A global geometric framework for nonlinear dimensionality reduction (2000) Science, 290 (5500), pp. 2319-2323Tu, Z., Yuille, A.L., Shape matching and recognition - Using generative models and informative features (2004) ECCV, pp. 195-209Van De Weijer, J., Schmid, C., Coloring local feature extraction (2006) ECCV, pp. 334-348Van Rijsbergen, C.J., (1979) Information Retrieval, , Butterworth-Heinemann, LondonWang, B., Jiang, J., Wang, W., Zhou, Z.-H., Tu, Z., Unsupervised metric fusion by cross diffusion (2012) CVPR, pp. 3013-3020Williams, A., Yoon, P., Content-based image retrieval using joint correlograms (2007) MTAP, 34 (2), pp. 239-248. , AugustWolf, L., Hassner, T., Taigman, Y., Similarity scores based on background samples (2009) ACCV?09, pp. 88-97Wu, P., Manjunanth, B.S., Newsam, S.D., Shin, H.D., A texture descriptor for image retrieval and browsing (1999) CBAIVL, pp. 3-7Yang, X., Bai, X., Latecki, L.J., Tu, Z., Improving shape retrieval by learning graph transduction (2008) ECCV, 4, pp. 788-801Yang, X., Koknar-Tezel, S., Latecki, L.J., Locally constrained diffusion process on locally densified distance spaces with applications to shape retrieval (2009) CVPR, pp. 357-364Yang, X., Prasad, L., Latecki, L., Affinity learning with diffusion on tensor product graph (2012) PAMI, PP (99), p. 1Young, H.P., An axiomatization of borda?s rule (1974) Journal of Economic Theory, 9 (1), pp. 43-52Zhang, S., Yang, M., Cour, T., Yu, K., Metaxas, D.N., Query specific fusion for image retrieval ECCV, 2012, pp. 660-673