On the Resemblance and Containment of Documents
SEQUENCES '97 Proceedings of the Compression and Complexity of Sequences 1997
Automatic multimedia cross-modal correlation discovery
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Manifold-ranking based image retrieval
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Graph based multi-modality learning
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Manifold-ranking-based keyword propagation for image retrieval
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Optimizing multi-graph learning: towards a unified video annotation scheme
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MapReduce: simplified data processing on large clusters
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ContextSeer: context search and recommendation at query time for shared consumer photos
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Pairwise document similarity in large collections with MapReduce
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Brute force and indexed approaches to pairwise document similarity comparisons with MapReduce
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Query expansion for hash-based image object retrieval
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Ranking and semi-supervised classification on large scale graphs using map-reduce
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Multi-label boosting for image annotation by structural grouping sparsity
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PEGASUS: mining peta-scale graphs
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Lost in binarization: query-adaptive ranking for similar image search with compact codes
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Multi-layer graph-based semi-supervised learning for large-scale image datasets using mapreduce
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Editor's Choice Article: Sparse feature selection based on graph Laplacian for web image annotation
Image and Vision Computing
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Semi-supervised learning (SSL) is widely-used to explore the vast amount of unlabeled data in the world. Over the decade, graph-based SSL becomes popular in automatic image annotation due to its power of learning globally based on local similarity. However, recent studies have shown that the emergence of large-scale datasets challenges traditional methods. On the other hand, most previous works have concentrated on single-label annotation, which may not describe image contents well. To remedy the deficiencies, this paper proposes a new graph-based SSL technique with multi-label propagation, leveraging the distributed computing power of the MapReduce programming model. For high learning performance, the paper further presents both a multi-layer learning structure and a tag refinement approach, where the former unifies both visual and textual information of image data during learning, while the latter simultaneously suppresses noisy tags and emphasizes the other tags after learning. Experimental results based on a medium-scale and a large-scale image datasets show the effectiveness of the proposed methods.