DocumentCode :
54065
Title :
Weakly Supervised Multi-Graph Learning for Robust Image Reranking
Author :
Cheng Deng ; Rongrong Ji ; Dacheng Tao ; Xinbo Gao ; Xuelong Li
Author_Institution :
Sch. of Electron. Eng., Xidian Univ., Xi´an, China
Volume :
16
Issue :
3
fYear :
2014
fDate :
Apr-14
Firstpage :
785
Lastpage :
795
Abstract :
Visual reranking has been widely deployed to refine the traditional text-based image retrieval. Its current trend is to combine the retrieval results from various visual features to boost reranking precision and scalability. And its prominent challenge is how to effectively exploit the complementary property of different features. Another significant issue raises from the noisy instances, from manual or automatic labels, which makes the exploration of such complementary property difficult. This paper proposes a novel image reranking by introducing a new Co-Regularized Multi- Graph Learning (Co-RMGL) framework, in which intra-graph and inter-graph constraints are integrated to simultaneously encode the similarity in a single graph and the consistency across multiple graphs. To deal with the noisy instances, weakly supervised learning via co-occurred visual attribute is utilized to select a set of graph anchors to guide multiple graphs alignment and fusion, and to filter out those pseudo labeling instances to highlight the strength of individual features. After that, a learned edge weighting matrix from a fused graph is used to reorder the retrieval results. We evaluate our approach on four popular image retrieval datasets and demonstrate a significant improvement over state-of-the-art methods.
Keywords :
feature extraction; graph theory; image denoising; image fusion; image retrieval; learning (artificial intelligence); matrix algebra; Co-RMGL frameowrk; co-occurred visual attribute; coregularized multigraph learning framework; graph anchors; image retrieval datasets; intergraph constraints; intragraph constraints; learned edge weighting matrix; multiple graph alignment; multiple graph fusion; noisy instances; pseudolabeling instances; robust image reranking; text-based image retrieval; visual features; visual reranking precision; visual reranking scalibility; weakly supervised multigraph learning; Educational institutions; Fuses; Labeling; Noise measurement; Robustness; Semantics; Visualization; Attributes; co-occurred patterns; multiple graphs; visual reranking; weakly supervised learning;
fLanguage :
English
Journal_Title :
Multimedia, IEEE Transactions on
Publisher :
ieee
ISSN :
1520-9210
Type :
jour
DOI :
10.1109/TMM.2014.2298841
Filename :
6705682
Link To Document :
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