Tri-Clustered Tensor Completion For Social-Aware Image Tag Refinement

Keywords

Social image tag refinement; tensor completion; tri-clustering

Abstract

Social image tag refinement, which aims to improve tag quality by automatically completing the missing tags and rectifying the noise-corrupted ones, is an essential component for social image search. Conventional approaches mainly focus on exploring the visual and tag information, without considering the user information, which often reveals important hints on the (in)correct tags of social images. Towards this end, we propose a novel tri-clustered tensor completion framework to collaboratively explore these three kinds of information to improve the performance of social image tag refinement. Specifically, the inter-relations among users, images and tags are modeled by a tensor, and the intra-relations between users, images and tags are explored by three regularizations respectively. To address the challenges of the super-sparse and large-scale tensor factorization that demands expensive computing and memory cost, we propose a novel tri-clustering method to divide the tensor into a certain number of sub-tensors by simultaneously clustering users, images and tags into a bunch of tri-clusters. And then we investigate two strategies to complete these sub-tensors by considering (in)dependence between the sub-tensors. Experimental results on a real-world social image database demonstrate the superiority of the proposed method compared with the state-of-the-art methods.

Publication Date

8-1-2017

Publication Title

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volume

39

Issue

8

Number of Pages

1662-1674

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/TPAMI.2016.2608882

Socpus ID

85028330194 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85028330194

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