Abstract
We introduce in this paper a novel image annotation approach based on support vector machines (SVMs) and a new class of kernels referred to as context-dependent. The method goes beyond the naive use of the intrinsic low level features (such as color, texture, shape, etc.) and context-free kernels, in order to design a kernel function applicable to interconnected databases such as social networks. The main contribution of our method includes (i) a variational approach which helps designing this function using both intrinsic features and the underlying contextual information resulting from different links and (ii) the proof of convergence of our kernel to a positive definite fixed-point, usable for SVM training and other kernel methods. When plugged in SVMs, our context-dependent kernel consistently improves the performance of image annotation, compared to context-free kernels, on hundreds of thousands of Flickr images.
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References
Carneiro, G., Vasconcelos, N.: Formulating semantic image annotation as a supervised learning problem. In: Proc. of CVPR (2005)
Li, J., Wang, J.Z.: Automatic linguistic indexing of pictures by a statistical modeling approach. IEEE Trans. on PAMI 25(9), 1075–1088 (2003)
Barnard, K., Duygululu, P., Forsyth, D., Blei, D., Jordan, M.: Matching words and pictures. The Journal of Machine Learning Research (2003)
Monay, F., GaticaPerez, D.: PLSA-based Image AutoAnnotation: Constraining the Latent Space. In: Proc. of ACM International Conference on Multimedia (2004)
Gao, Y., Fan, J., Xue, X., Jain, R.: Automatic Image Annotation by Incorporating Feature Hierarchy and Boosting to Scale up SVM Classifiers. In: Proc. of ACM MULTIMEDIA (2006)
Jeon, J., Lavrenko, V., Manmatha, R.: Automatic image annotation and retrieval using cross-media relevance models. In: Proc. of ACM SIGIR, pp. 119–126 (2003)
Lavrenko, V., Manmatha, R., Jeon, J.: A model for learning the semantics of pictures. In: Proc. of NIPS (2004)
Feng, S., Manmatha, R., Lavrenko, V.: Multiple Bernoulli relevance models for image and video annotation. In: Proc. of ICCV, pp. 1002–1009 (2004)
Liu, J., Wang, B., Li, M., Li, Z., Ma, W., Lu, H., Ma, S.: Dual cross-media relevance model for image annotation. In: Proc. of ACM MULTIMEDIA, pp. 605–614 (2007)
Wan, X., Yang, J., Xiao, J.: Manifold-ranking based topic-focused multi-document summarization. In: Proc. of IJCAI, pp. 2903–2908 (2007)
Zhou, D., Weston, J., Gretton, A., Bousquet, O., Schölkopf, B.: Ranking on data manifolds. In: Proc. of NIPS (2004)
Liu, J., Li, M., Liu, Q., Lu, H., Ma, S.: Image annotation via graph learning. Pattern Recognition 42(2), 218–228 (2009)
Liu, J., Li, M., Ma, W., Liu, Q., Lu, H.: An adaptive graph model for automatic image annotation. In: Proc. of ACM International Workshop on Multimedia Information Retrieval, pp. 61–70 (2006)
Srikanth, M., Varner, J., Bowden, M., Moldovan, D.: Exploiting ontologies for automatic image annotation. In: Proc. of SIGIR, pp. 552–558 (2005)
Wu, Y., Chang, E.Y., Tseng, B.L.: Multimodal metadata fusion using causal strength. In: Proc. of ACM MULTIMEDIA, pp. 872–881 (2005)
Miller, G.A.: Wordnet: a lexical database for English. ACM Commun. 38(11), 39–41 (1995)
Wang, C., Jing, F., Zhang, L., Zhang, H.J.: Image annotation refinement using random walk with restarts. In: Proc. of ACM MULTIMEDIA, pp. 647–650 (2006)
Jin, Y., Khan, L., Wang, L., Awad, M.: Image annotations by combining multiple evidence & wordNet. In: Proc. of ACM MULTIMEDIA, pp. 706–715 (2005)
Cilibrasi, R., Vitanyi, P.M.B.: The google similarity distance. IEEE Transactions on Knowledge and Data Engineering (2007)
Wu, L., Hua, X., Yu, N., Ma, W., Li, S.: Flickr distance. In: Proc. of ACM MULTIMEDIA (2008)
Wang, Y., Gong, S.: Translating Topics to Words for Image Annotation. In: Proc. of ACM CIKM (2007)
Lu, Z., Ip, H.H.S., He, Q.: Context-Based Multi-Label Image Annotation. In: Proc. of ACM CIVR (2009)
Boser, B., Guyon, I., Vapnik, V.: An training algorithm for optimal margin classifiers. In: Fifth Annual ACM Workshop on Computational Learning Theory, Pittsburgh (1992)
Vapnik, V.: Statistical Learning Theory. A Wiley-Interscience Publication, Hoboken (1998)
Wang, C., Yan, S., Zhang, L., Zhang, H.: Multi-Label Sparse Coding for Automatic Image Annotation. In: Proc. of CVPR (2009)
Duygulu, P., Barnard, K., de Freitas, J.F.G., Forsyth, D.: Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2353, pp. 97–112. Springer, Heidelberg (2002)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. In: Proc. of CVPR (2006)
Gallagher, A.C., Neustaedter, C.G., Cao, L., Luo, J., Chen, T.: Image Annotation Using Personal Calendars as Context. In: Proc. of ACM Multimedia (2008)
Cao, L., Luo, J., Huang, T.S.: Annotating Photo Collection by Label Propagation According to Multiple Similarity Cues. In: Proc. of ACM Multimedia (2008)
Sahbi, H., Audibert, J.-Y.: Social network kernels for image ranking and retrieval. In Technical Report, N 2009D009, TELECOM ParisTech (March 2009)
Shawe-Taylor, J., Cristianini, N.: Support Vector Machines and other kernel-based learning methods. Cambridge University Press, Cambridge (2000)
Yang, Y.H., Wu, P.T., Lee, C.W., Lin, K.H., Hsu, W.H., Chen, H.: ContextSeer: Context Search and Recommendation at Query Time for Shared Consumer Photos. In: Proc. of ACM Multimedia (2008)
Haussler, D.: Convolution Kernels on Discrete Structures. In Technical Report UCSC-CRL-99-10, University of California in Santa Cruz, Computer Science Department (July 1999)
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Sahbi, H., Li, X. (2011). Context-Based Support Vector Machines for Interconnected Image Annotation. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19315-6_17
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DOI: https://doi.org/10.1007/978-3-642-19315-6_17
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