caa a22 4500 605447918 CHVBK 20210128100134.0 cr unu---uuuuu 210128e20150801xx s 000 0 eng 10.1007/s11042-014-1873-x doi (NATIONALLICENCE)springer-10.1007/s11042-014-1873-x An efficient framework of Bregman divergence optimization for co-ranking images and tags in a heterogeneous network [Elektronische Daten] [Lin Wu, Xiaodi Huang, Chengyuan Zhang, John Shepherd, Yang Wang] Graph-based ranking is an effective way of ranking images by making use of the graph structure. However, its applications are usually limited to individual image graphs, which are derived from self-contained features of images. Nowadays, many images in social web sites are often associated with semantic information (i.e., tags). Ranking of these orderless tags is helpful in understanding and retrieving images, thus, improving the overall ranking performance if their mutual reinforcement is considered. Unlike previous work only focusing on individual image or tag graphs, in this paper, we investigate the problem of co-ranking images and tags in a heterogeneous network. Considering that ranking on images and tags can be conducted simultaneously, we present a novel co-ranking method with random walks that is able to significantly improve the ranking effectiveness on both images and tags. We further improve the performance of our algorithm in computational complexity and the out-of-sample problem. This is achieved by casting the co-ranking as a Bregman divergence optimization, under which we transform the original random walks into an equivalent optimal kernel matrix learning problem. Extensive experiments conducted on three benchmarks show that our approach outperforms the state-of-the-art local ranking approaches and scales on large-scaled databases. Springer Science+Business Media New York, 2014 Co-ranking nationallicence Random walks nationallicence Heterogeneous network nationallicence Bregman divergence nationallicence Wu Lin School of Computer Science and Engineering, The University of New South Wales, 2052, Sydney, Australia aut Huang Xiaodi School of Computing and Mathematics, Charles Sturt University, 2640, Albury, NSW, Australia aut Zhang Chengyuan School of Computer Science and Engineering, The University of New South Wales, 2052, Sydney, Australia aut Shepherd John School of Computer Science and Engineering, The University of New South Wales, 2052, Sydney, Australia aut Wang Yang School of Computer Science and Engineering, The University of New South Wales, 2052, Sydney, Australia aut Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/15(2015-08-01), 5635-5660 1380-7501 74:15<5635 2015 74 11042 https://doi.org/10.1007/s11042-014-1873-x text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s11042-014-1873-x text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Wu Lin School of Computer Science and Engineering, The University of New South Wales, 2052, Sydney, Australia aut NATIONALLICENCE 700 1- Huang Xiaodi School of Computing and Mathematics, Charles Sturt University, 2640, Albury, NSW, Australia aut NATIONALLICENCE 700 1- Zhang Chengyuan School of Computer Science and Engineering, The University of New South Wales, 2052, Sydney, Australia aut NATIONALLICENCE 700 1- Shepherd John School of Computer Science and Engineering, The University of New South Wales, 2052, Sydney, Australia aut NATIONALLICENCE 700 1- Wang Yang School of Computer Science and Engineering, The University of New South Wales, 2052, Sydney, Australia aut NATIONALLICENCE 773 0- Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/15(2015-08-01), 5635-5660 1380-7501 74:15<5635 2015 74 11042