Similarity space projection for web image search and annotation
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
High accuracy retrieval with multiple nested ranker
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Content-based image retrieval with the normalized information distance
Computer Vision and Image Understanding
Learning tag relevance by neighbor voting for social image retrieval
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
LS-MMRM '09 Proceedings of the First ACM workshop on Large-scale multimedia retrieval and mining
Learning social tag relevance by neighbor voting
IEEE Transactions on Multimedia
SIEVE: search images effectively through visual elimination
MCAM'07 Proceedings of the 2007 international conference on Multimedia content analysis and mining
Non-parametric kernel ranking approach for social image retrieval
Proceedings of the ACM International Conference on Image and Video Retrieval
Co-reranking by mutual reinforcement for image search
Proceedings of the ACM International Conference on Image and Video Retrieval
Mixture model based contextual image retrieval
Proceedings of the ACM International Conference on Image and Video Retrieval
Concept model-based unsupervised web image re-ranking
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Using the knowledge of object colors to segment images and improve web image search
Large Scale Semantic Access to Content (Text, Image, Video, and Sound)
Automatic Face Annotation in News Images by Mining the Web
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Retrieving and ranking unannotated images through collaboratively mining online search results
Proceedings of the 20th ACM international conference on Information and knowledge management
Pseudo relevance feedback based on iterative probabilistic one-class SVMs in web image retrieval
PCM'04 Proceedings of the 5th Pacific Rim Conference on Advances in Multimedia Information Processing - Volume Part II
Image similarity: from syntax to weak semantics
Multimedia Tools and Applications
Intelligent Social Media Indexing and Sharing Using an Adaptive Indexing Search Engine
ACM Transactions on Intelligent Systems and Technology (TIST)
Ranking content-based social images search results with social tags
AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
Joint-rerank: a novel method for image search reranking
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Content-Based re-ranking of text-based image search results
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
VISOR: towards on-the-fly large-scale object category retrieval
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
A heterogenous automatic feedback semi-supervised method for image reranking
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Recognizing human-human interaction activities using visual and textual information
Pattern Recognition Letters
Ranking consistency for image matching and object retrieval
Pattern Recognition
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Web image retrieval is a challenging task that requires efforts from image processing, link structure analysis, and web text retrieval. Since content-based image retrieval is still considered very difficult, most current large-scale web image search engines exploit text and link structure to "understand" the content of the web images. However, local text information, such as caption, filenames and adjacent text, is not always reliable and informative. Therefore,global information should be taken into account when a web image retrieval system makes relevance judgment. In this paper, we propose a re-ranking method to improve web image retrieval by reordering the images retrieved from an image search engine. The re-ranking process is based on a relevance model, which is a probabilistic model that evaluates the relevance of the HTML document linking to the image, and assigns a probability of relevance. The experiment results showed that the re-ranked image retrieval achieved better performance than original web image retrieval, suggesting the effectiveness of the re-ranking method. The relevance model is learned from the Internet without preparing any training data and independent of the underlying algorithm of the image search engines. The re-ranking process should be applicable to any image search engines with little effort.