Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
The Journal of Machine Learning Research
Web Image Retrieval Re-Ranking with Relevance Model
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
Learning Object Categories from Google"s Image Search
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Graph based multi-modality learning
Proceedings of the 13th annual ACM international conference on Multimedia
Semi-supervised learning with graphs
Semi-supervised learning with graphs
Learning subjective nouns using extraction pattern bootstrapping
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Video search reranking via information bottleneck principle
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Learning to rank: from pairwise approach to listwise approach
Proceedings of the 24th international conference on Machine learning
Video search reranking through random walk over document-level context graph
Proceedings of the 15th international conference on Multimedia
VisualRank: Applying PageRank to Large-Scale Image Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian video search reranking
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Real time google and live image search re-ranking
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Co-reranking by mutual reinforcement for image search
Proceedings of the ACM International Conference on Image and Video Retrieval
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Supervised reranking for web image search
Proceedings of the international conference on Multimedia
Learning to re-rank: query-dependent image re-ranking using click data
Proceedings of the 20th international conference on World wide web
Visual search reranking via adaptive particle swarm optimization
Pattern Recognition
Fusing heterogeneous modalities for video and image re-ranking
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Finding images of difficult entities in the long tail
Proceedings of the 20th ACM international conference on Information and knowledge management
Ensemble approach based on conditional random field for multi-label image and video annotation
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Visual reranking with local learning consistency
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Joint-rerank: a novel method for image search reranking
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Image search results refinement via outlier detection using deep contexts
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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Image reranking, which aims at enhancing the quality of keyword-based image search with the help of image features, recently has become attractive in image search community. A major challenging in this task is that image's visual features do not always well reflect image's semantic meaning. Thus, reranking methods only depending on visual features cannot guarantee to obtain good results. In addition, it is well known that the visual features of an image have strong/weak correlations with its surrounding text. Thus, it is expected that a model considering both visual features and its surrounding text can perform better than those only considering visual features. Motivated by this, in this paper, we propose the HAFSRerank--Heterogenous Automatic Feedback Semi-supervised Reranking method which makes use of both visual and textual features simultaneously during reranking. Specifically, in HAFSRerank, a multigraph is firstly constructed in which each node representing an image includes visual and textual features, and the parallel edges between them are weighted by intra-modal similarity and inter-modal similarity. A heterogenous complete graph is further derived from the multigraph. Then, an automatic feedback graph-based semi-supervised learning method is proposed to propagate the reranking scores on the complete graph, which can make use of the inter-modal similarity to update the weights of heterogenous graph automatically. Finally, the result of the semi-supervised learning is used to rerank the images. The experimental results show that HAFSRerank is superior or highly competitive to some state-of-the-art graph-based reranking methods. Moreover, the proposed reranking algorithm can be well interpreted by Bayesian theory, and does not require complex search models for special queries and any additional input from users.