Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
Learning low-rank kernel matrices
ICML '06 Proceedings of the 23rd international conference on Machine learning
Learning a Maximum Margin Subspace for Image Retrieval
IEEE Transactions on Knowledge and Data Engineering
Pagerank for product image search
Proceedings of the 17th international conference on World Wide Web
Introduction to Information Retrieval
Introduction to Information Retrieval
Co-ranking Authors and Documents in a Heterogeneous Network
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Proceedings of the 18th international conference on World wide web
Personalized tag recommendation using graph-based ranking on multi-type interrelated objects
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Semi-supervised distance metric learning for collaborative image retrieval and clustering
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Efficient manifold ranking for image retrieval
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
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Ranking on image search has attracted considerable attentions. Many graph-based algorithms have been proposed to solve this problem. Despite their remarkable success, these approaches are restricted to their separated image networks. To improve the ranking performance, one effective strategy is to work beyond the separated image graph by leveraging fruitful information from manual semantic labeling (i.e., tags) associated with images, which leads to the technique of co-ranking images and tags, a representative method that aims to explore the reinforcing relationship between image and tag graphs. The idea of co-ranking is implemented by adopting the paradigm of random walks. However, there are two problems hidden in co-ranking remained to be open: the high computational complexity and the problem of out-of-sample. To address the challenges above, in this paper, we cast the co-ranking process into a Bregman divergence optimization framework under which we transform the original random walk into an equivalent optimal kernel matrix learning problem. Enhanced by this new formulation, we derive a novel extension to achieve a better performance for both in-sample and out-of-sample cases. Extensive experiments are conducted to demonstrate the effectiveness and efficiency of our approach.