Tag Refinement for User-Contributed Images via Graph Learning and Nonnegative Tensor Factorization PROJECT TITLE :Tag Refinement for User-Contributed Images via Graph Learning and Nonnegative Tensor FactorizationABSTRACT:Social image tagging systems mostly suffer from poor performance for image retrieval because of the noisy and incomplete correspondences between user-contributed pictures and their associated tags. During this letter, we have a tendency to aim to refine tag allocations within the social tagging data provided by these systems. In particular, we have a tendency to propose to harness the tagged and untagged information with a 2-stage strategy according to completely different varieties of information relations, i.e. item similarity outlined by prior information and item co-incidence learned from data statistics. To unravel the sparsity downside, we have a tendency to first introduce a new graph learning (GL) method for enriching the tagging knowledge in line with item similarities. Then, we tend to develop a method of nonnegative tensor factorization (NTF) for learning more coherent ternary relations among users, images and tags coupled by the manifold constraints learned from item co-occurrences. Experimental results with the tagging data from the NUS-WIDE dataset have been reported to validate the effectiveness of the proposed methodology. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Mathematics and Physics Build a New Future for Secure Communication [Guest editors' introduction] Spatiotemporal Detection and Analysis of Urban Villages in Mega City Regions of China Using High-Resolution Remotely Sensed Imagery
