Abstract
Social network study has become an important topic in many research fields. Early works on social network analysis focus on real world social interactions in either human society or animal world. With the explosion of Internet data, social network researchers start to pay more attention to the tremendous amount of online social network data. There are ample space for exploring social network research on large-scale online visual content. In this paper, we focus on studying multi-label collective classification problem and develop a model that can harness the mutually beneficial information among the visual appearance, related semantic content and the social network structure simultaneously. Our algorithm is then tested on CelebrityNet, a social network constructed by inferring implicit relationship of people based on online multimedia content. We apply our model to a few important multimedia applications such as image annotation and community classification. We demonstrate that our algorithm significantly outperforms traditional methods on community classification and image annotation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR, p. 886 (2005)
Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: Object Detection with Discriminatively Trained Part Based Models. JAIRÂ 29 (2007)
Lin, Y., Lv, F., Zhu, S., Yang, M., Cour, T., Yu, K., Cao, L., Huang, T.: Large-scale image classification: fast feature extraction and svm training. In: CVPR, pp. 1689–1696 (2011)
Deng, J., Berg, A.C., Li, K., Fei-Fei, L.: What does classifying more than 10,000 image categories tell us? In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 71–84. Springer, Heidelberg (2010)
Wu, Z., Ke, Q., Isard, M., Sun, J.: Bundling features for large scale partial-duplicate web image search. In: CVPR, pp. 25–32 (2009)
Jégou, H., Douze, M., Schmid, C.: Improving bag-of-features for large scale image search. IJCV 87, 316–336 (2010)
Cao, L., Yu, J., Luo, J., Huang, T.: Enhancing semantic and geographic annotation of web images via logistic canonical correlation regression. In: ACM MM (2009)
Weston, J., Bengio, S., Usunier, N.: Large scale image annotation: Learning to rank with joint word-image embeddings. Machine learning 81, 21–35 (2010)
Konstas, I., Stathopoulos, V., Jose, J.: On social networks and collaborative recommendation. In: SIGIR, pp. 195–202. ACM (2009)
Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems 30, 107–117 (1998)
Stone, Z., Zickler, T., Darrell, T.: Toward large-scale face recognition using social network context. Proceedings of the IEEE 98, 1408–1415 (2010)
Wang, G., Gallagher, A., Luo, J., Forsyth, D.: Seeing people in social context: Recognizing people and social relationships. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 169–182. Springer, Heidelberg (2010)
Zhuang, J., Mei, T., Hoi, S., Hua, X., Li, S.: Modeling social strength in social media community via kernel-based learning. In: ACM MM (2011)
Lu, Q., Getoor, L.: Link-based classification. In: ICML (2003)
Ding, L., Yilmaz, A.: Learning relations among movie characters: A social network perspective. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 410–423. Springer, Heidelberg (2010)
Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009, Part II. LNCS, vol. 5782, pp. 254–269. Springer, Heidelberg (2009)
Liu, X., Shi, Z., Li, Z., Wang, X., Shi, Z.: Sorted label classifier chains for learning images with multi-label. In: ACM MM (2010)
Palla, G., Derényi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435, 814–818 (2005)
Vishwanathan, S.V.N., Sun, Z., Theera-Ampornpunt, N.: Multiple kernel learning and the smo algorithm. In: NIPS (2010)
McDowell, L.K., Gupta, K.M., Aha, D.W.: Cautious inference in collective classification. In: AAAI, Vancouver, Canada, pp. 596–601 (2007)
Boutell, M.R., Luo, J., Shen, X., Brown, C.M.: Learning multi-label scene classification. Pattern Recognition 37, 1757–1771 (2004)
Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: LIBLINEAR: a library for large linear classification (2008)
Ghamrawi, N., McCallum, A.: Collective multi-label classification. In: CIKM, Bremen, Germany, pp. 195–200 (2005)
Kang, F., Jin, R., Sukthankar, R.: Correlated label propagation with application to multi-label learning. In: CVPR, New York, NY, pp. 1719–1726 (2006)
Liu, Y., Jin, R., Yang, L.: Semi-supervised multi-label learning by constrained non-negative matrix factorization. In: AAAI, Boston, MA, pp. 421–426 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Li, LJ., Kong, X., Yu, P.S. (2014). Visual Recognition by Exploiting Latent Social Links in Image Collections. In: Gurrin, C., Hopfgartner, F., Hurst, W., Johansen, H., Lee, H., O’Connor, N. (eds) MultiMedia Modeling. MMM 2014. Lecture Notes in Computer Science, vol 8325. Springer, Cham. https://doi.org/10.1007/978-3-319-04114-8_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-04114-8_11
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-04113-1
Online ISBN: 978-3-319-04114-8
eBook Packages: Computer ScienceComputer Science (R0)