Dynamic Reconstruct for Network Photograph Exploration
||International Journal of Computer Trends and Technology (IJCTT)||
|© - Issue 2012 by IJCTT Journal|
|Year of Publication : 2012|
|Authors :T.Rajesh, A.Ravi.|
T.Rajesh, A.Ravi. "Dynamic Reconstruct for Network Photograph Exploration"International Journal of Computer Trends and Technology (IJCTT),V3(3):1032-1038 Issue 2012 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract: -Photograph sear reconstruct methods usually fail to capture the user’s intention when the query termism ambiguous. Therefore, reconstruct with user interactions, or active reconstruct, is highly demanded to effect very improve the search performance. The essential problem in active reconstruct is how to target the user’s intention. To complete this goal, this paper presents a structural information based sample selection strategy to reduce the user’s labeling efforts. Furthermore, to localize the user’s intention in the visual feature e space, a novel local-global discriminative dimension reduction algorithmic proposed. In this algorithm, a sub manifold is leer need by transferring the local geometry and the discriminate vet information from the labeled photographs to the whole (global) photograph database. Experiments on both synthetic datasets and a real Network photograph sear chdatasetde menstruate he effectiveness of the proposed active reconstruct scheme, including both the structural information based active sample selection strategy and the local-global discriminative dimension reduction algorithm.
 D. Cai, X. He, J. Han, and H.-J. Zhang, “Orthogonal laplacianf aces for face recognition, ” IEEE Trans. Image Process., pp. 3608–3614, 2006.
 D. Cai, X. He, and J. Han, UsingGraphModelfor Face Analysis, Tech. Rep.,2005, Comput. Sci. Dept.Univ. Illinois, Urbana-Champaign.
 D. Cai, X. He, and J. Han, “Semisuperviseddiscriminantanalysis,” in Proc.IEEE Int. Conf.ComputerVision , 2007, pp. 1–8.
 E. Y. Chang, S. Tong, K. Goh, and C.-W. Chang, “Support vector machine concept-dependent dynamic learning for photograph retrieval, ” IEEE Trans. Multimedia, 2005.
 H.-T.Chen,H.-W. Chang, and T. L. Liu, “Local discriminant embed- ding and its variants, ” in IEEE Int. Conf.ComputerVision and Pattern Recognition , 2005, pp. 846– 853.
 J. Cui, F. Wen, and X. Tang, “Real time google and live photograph exploration re-ranking, ” presented at the ACM Int. Conf.Multimedia, 2008.
 R. A. Fisher, “The use of multiplemeasurements in taxonomic problems,” Ann. Eugen., pp. 179–188, 1936.
 Y. Fuand T. Huang, “Photograph classification using correlation tensor analysis,” IEEE Trans. Ima ge Process., pp. 226–234, 2008.
 Y. Fu, S. Yan, and T. Huang, “Correlation metric for generalized feature extraction, ” IEEE Trans. Pattern Anal. Mach. Intell. , pp. 2229–2235, 2008.
 X. He,D. Cai, and J. Han, “Learninga maximummargin subspace for photograph retrieval, ” IEEE Trans. Knowl. DataEng. , pp. 189–201, 2008.
 X. Heand P. Niyogi,“Locality preserving projections, ” Adv. Neural Inf. Process. Syst. , 2003.
Keywords:Active reconstructs, local-global discriminative (LGD) dimension reduction, structural information (SInfo) based active sample selection, network photograph sear reconstruct.