Abstract: With the rapid development of the Internet, recent years have seen the explosive growth of social media. This brings great challenges in performing efficient and accurate image retrieval on a large scale. Recent work shows that using hashing methods to embed high-dimensional image features and tag information into Hamming space provides a powerful way to index large collections of social images. By learning hash codes through a spectral graph partitioning algorithm, spectral hashing (SH) has shown promising performance among various hashing approaches. However, it is incomplete to model the relations among images only by pairwise simple graphs which ignore the relationship in a higher order. In this paper, we utilize a probabilistic hypergraph model to learn hash codes for social image retrieval. A probabilistic hypergraph model offers a higher order representation among social images by connecting more than two images in one hyperedge. Unlike a normal hypergraph model, a probabilistic hypergraph model considers not only the grouping information, but also the similarities between vertices in hyperedges. Experiments on Flickr image datasets verify the performance of our proposed approach.

基于概率超图哈希编码的社交图像检索研究

[1]Andoni, A., Indyk, P., 2006. Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. 47th Annual IEEE Symp. on Foundations of Computer Science, p.459-468.

[2]Arya, S., Mount, D.M., Netanyahu, N.S., et al., 1998. An optimal algorithm for approximate nearest neighbor searching fixed dimensions. JACM, 45(6):891-923.

[3]Bengio, Y., Delalleau, O., le Roux, N., et al., 2004. Learning eigenfunctions links spectral embedding and kernel PCA. Neur. Comput., 16(10):2197-2219.

[4]Chua, T.S., Tang, J., Hong, R., et al., 2009. NUS-WIDE: a real-world web image database from National University of Singapore. Proc. ACM Int. Conf. on Image and Video Retrieval, p.48.

[5]Gao, Y., Wang, M., Zha, Z.J., et al., 2013. Visual-textual joint relevance learning for tag-based social image search. IEEE Trans. Image Process., 22(1):363-376.

[6]He, J., Li, M., Zhang, H.J., et al., 2004. Manifold-ranking based image retrieval. Proc. 12th Annual ACM Int. Conf. on Multimedia, p.9-16.

[7]He, J., Li, M., Zhang, H.J., et al., 2006. Generalized manifold-ranking-based image retrieval. IEEE Trans. Image Process., 15(10):3170-3177.

[8]Hinton, G.E., Salakhutdinov, R.R., 2006. Reducing the dimensionality of data with neural networks. Science, 313(5786):504-507.

[9]Huang, Y., Liu, Q., Zhang, S., et al., 2010. Image retrieval via probabilistic hypergraph ranking. IEEE Conf. on Computer Vision and Pattern Recognition, p.3376-3383.

[10]Jiang, Y.G., Ngo, C.W., 2008. Bag-of-visual-words expansion using visual relatedness for video indexing. Proc. 31st Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, p.769-770.

[11]Li, P., Wang, M., Cheng, J., et al., 2013. Spectral hashing with semantically consistent graph for image indexing. IEEE Trans. Multimedia, 15(1):141-152.

[12]Liu, H., le Pendu, P., Jin, R., et al., 2011. A hypergraph-based method for discovering semantically associated itemsets. IEEE 11th Int. Conf. on Data Mining, p.398-406.

[13]Lowe, D.G., 2004. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis., 60(2):91-110.

[14]Salakhutdinov, R., Hinton, G.E., 2007. Learning a nonlinear embedding by preserving class neighborhood structure. Int. Conf. on Artificial Intelligence and Statistics, p.412-419.

[15]Shakhnarovich, G., Viola, P., Darrel, T., 2003. Fast pose estimation with parameter-sensitive hashing. Proc. 9th IEEE Int. Conf. on Computer Vision, p.750-757.

[16]Silpa-Anan, C., Hartley, R., 2008. Optimised KD-trees for fast image descriptor matching. IEEE Conf. on Computer Vision and Pattern Recognition, p.1-8.

[17]Torralba, A., Fergus, R., Weiss, Y., 2008. Small codes and large image databases for recognition. IEEE Conf. on Computer Vision and Pattern Recognition, p.1-8.

[18]Weiss, Y., Torralba, A., Fergus, R., 2008. Spectral hashing. 21st Advances in NIPS, p.1753-1760.

[19]Xia, S., Hancock, E.R., 2008. Clustering using class specific hyper graphs. Structural, Syntactic, and Statistical Pattern Recognition, p.318-328.

[20]Yang, J., Jiang, Y.G., Hauptmann, A.G., et al., 2007. Evaluating bag-of-visual-words representations in scene classification. Proc. Int. Workshop on Multimedia Information Retrieval, p.197-206.

[21]Yu, J., Tao, D., Wang, M., 2012. Adaptive hypergraph learning and its application in image classification. IEEE Trans. Image Process., 21(7):3262-3272.

[22]Zass, R., Shashua, A., 2008. Probabilistic graph and hypergraph matching. IEEE Conf. on Computer Vision and Pattern Recognition, p.1-8.

[23]Zhang, D., Wang, J., Cai, D., et al., 2010. Self-taught hashing for fast similarity search. Proc. 33rd Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, p.18-25.

[24]Zhou, D., Bousquet, O., Lal, T.N., et al., 2004. Learning with local and global consistency. 17th Advances in NIPS, p.321-328.

[25]Zhou, D., Huang, J., Schölkopf, B., 2006. Learning with hypergraphs: clustering, classification, and embedding. 19th Advances in NIPS, p.1601-1608.

[26]Zhuang, Y., Liu, Y., Wu, F., et al., 2011. Hypergraph spectral hashing for similarity search of social image. Proc. 19th ACM Int. Conf. on Multimedia, p.1457-1460.