Abstract
This paper proposes a method for involving domain knowledge in the construction of summaries of large collections of images. This is accomplished by using a multi-class kernel alignment strategy in order to learn a kernel function that incorporates domain knowledge (class labels). The kernel function is the basis of a clustering algorithm that generates a subset, the summary, of the image collection. The method was tested with a subset of the Corel image collection using a summarization quality measure based on information theory. Experimental results show that it is possible to improve the quality of the summary when domain knowledge is involved.
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Alpaydin, E.: Introduction to Machine Learning. MIT Press, Cambridge (2004)
Cai, D., He, X., Li, Z., Ma, W.-Y., Wen, J.-R.: Hierarchical clustering of www image search results using visual, textual and link information. In: Proceedings of the 12th annual ACM international conference on Multimedia, pp. 952–959 (2004)
Chen, J.-Y., Bouman, C.A., Dalton, J.C.: Hierarchical browsing and search of large image databases. IEEE Transactions on Image Processing 9(3), 442–455 (2000)
Joshi, D., Li, J., Wang, J.Z., Datta, R.: Image retrieval: Ideas, influences, and trends of the new age. ACM Comput. Surv. 40(2), 1–60 (2008)
Deng, D.: Content-based image collection summarization and comparison using self-organizing maps. Pattern Recognition 40(2), 718–727 (2007)
Gao, B., Liu, T.-Y., Qin, T., Zheng, X., Cheng, Q.-S., Ma, W.-Y.: Web image clustering by consistent utilization of visual features and surrounding texts. In: MULTIMEDIA 2005: Proceedings of the 13th annual ACM international conference on Multimedia, pp. 112–121. ACM, New York (2005)
Nguyen, G.P., Worring, M.: Interactive access to large image collections using similarity-based visualization. Journal of Visual Languages & Computing 19(2), 203–224 (2008)
Shawe Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)
Simon, I., Snavely, N., Seitz, S.M.: Scene summarization for online image collections. In: IEEE 11th International Conference on Computer Vision, 2007 (ICCV 2007), pp. 1–8 (2007)
Stan, D., Sethi, I.K.: eid: a system for exploration of image databases. Inf. Process. Manage. 39(3), 335–361 (2003)
Torgerson, M.S.: Multidimensional scaling: I. theory and method. Psychometrika 17(4), 401–419 (1958)
Vert, R.: Designing a m-svm kernel for protein secondary structure prediction. Master’s thesis, DEA informatique de Lorraine (2002)
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© 2009 Springer-Verlag Berlin Heidelberg
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Camargo, J.E., González, F.A. (2009). A Multi-class Kernel Alignment Method for Image Collection Summarization. In: Bayro-Corrochano, E., Eklundh, JO. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2009. Lecture Notes in Computer Science, vol 5856. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10268-4_64
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DOI: https://doi.org/10.1007/978-3-642-10268-4_64
Publisher Name: Springer, Berlin, Heidelberg
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