Abstract
Manifold Ranking (MR) is one of the most popular graph-based ranking methods and has been widely used for information retrieval. Due to its ability to capture the geometric structure of the image set, it has been successfully used for image retrieval. The existing approaches that use manifold ranking rely only on a single image manifold. However, such methods may not fully discover the geometric structure of the image set and may lead to poor precision results. Motivated by this, we propose a novel method named Multi-Manifold Ranking (MMR) which embeds multiple image manifolds each constructed using a different image feature. We propose a novel cost function that is minimized to obtain the ranking scores of the images. Our proposed multi-manifold ranking has a better ability to explore the geometric structure of image set as demonstrated by our experiments. Furthermore, to improve the efficiency of MMR, a specific graph called anchor graph is incorporated into MMR. The extensive experiments on real world image databases demonstrate that MMR outperforms existing manifold ranking based methods in terms of quality and has comparable running time to the fastest MR algorithm.
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References
Christopher, P.R., Manning, D., Schütze, H.: An introduction to information Retrieval. Cambridge University Press (2009)
Dalal, N., Triggs, B.: Histogram of oriented gradients for human detection. In: CVPR, pp. 886–893 (2005)
Wang, M., et al.: Unified video annotation via multigraph learning. IEEE Trans. Circuits Syst. Video Techn. 19(5), 733–746 (2009)
He, J., Li, M., Zhang, H., Tong, H., Zhang, C.: Manifold-ranking based image retrieval. In: ACM Multimedia, pp. 9–16 (2004)
Huang, J., Kumar, S.R., Mitra, M., Jing Zhu, W.: Spatial color indexing and applications. International Journal of Computer Vision 35(3), 245–268 (1999)
Huang, Y., Liu, Q., Zhang, S., Metaxas, D.N.: Image retrieval via probabilistic hypergraph ranking. In: CVPR, pp. 3376–3383 (2010)
Liu, D., Hua, X.-S., Yang, L., Wang, M., Zhang, H.-J.: Tag ranking. In: ACM WWW, pp. 351–360 (2009)
Liu, W., He, J., Chang, S.-F.: Large graph construction for scalable semi-supervised learning. In: ICML, pp. 679–686 (2010)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Manjunath, B.S., Rainer Ohm, J., Vasudevan, V.V., Yamada, A.: Color and texture descriptors. IEEE Transactions on Circuits and Systems for Video Technology 11, 703–715 (1998)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)
Swain, M.J., Ballard, D.H.: Color indexing. International Journal of Computer Vision 7(1), 11–32 (1991)
Ulges, A., Worring, M., Breuel, T.M.: Learning visual contexts for image annotation from flickr groups. IEEE Transactions on Multimedia 13(2), 330–341 (2011)
van de Sande, K.E.A., Gevers, T., Snoek, C.G.M.: Evaluating color descriptors for object and scene recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(9), 1582–1596 (2010)
Wan, X., Yang, J., Xiao, J.: Manifold-ranking based topic-focused multi-document summarization. In: IJCAI, pp. 2903–2908 (2007)
Williams, C.K.I., Seeger, M.: Using the nyström method to speed up kernel machines. In: NIPS, pp. 682–688 (2000)
Wu, J., Rehg, J.M.: Centrist: A visual descriptor for scene categorization. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(8), 1489–1501 (2011)
Xu, B., Bu, J., Chen, C., Cai, D., He, X., Liu, W., Luo, J.: Efficient manifold ranking for image retrieval. In: ACM SIGIR, pp. 525–534 (2011)
Yuan, X., Hua, X.-S., Wang, M., Wu, X.: Manifold-ranking based video concept detection on large database and feature pool. In: ACM Multimedia, pp. 623–626 (2006)
Zhou, D., Bousquet, O., Lal, T.N., Weston, J., Schölkopf, B.: Learning with local and global consistency. In: NIPS, pp. 592–602 (2003)
Zhou, D., Weston, J., Gretton, A., Bousquet, O., Schölkopf, B.: Ranking on data manifolds. In: NIPS (2003)
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Wang, Y., Cheema, M.A., Lin, X., Zhang, Q. (2013). Multi-Manifold Ranking: Using Multiple Features for Better Image Retrieval. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7819. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37456-2_38
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DOI: https://doi.org/10.1007/978-3-642-37456-2_38
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