The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
A novel relevance feedback technique in image retrieval
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 2)
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
CueFlik: interactive concept learning in image search
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Pagerank for product image search
Proceedings of the 17th international conference on World Wide Web
Real time google and live image search re-ranking
MM '08 Proceedings of the 16th ACM international conference on Multimedia
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Topic based photo set retrieval using user annotated tags
Multimedia Tools and Applications
Multimedia search reranking: A literature survey
ACM Computing Surveys (CSUR)
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Modern image search engines such as Google, Yahoo!, Microsoft Live image search are all text metaword based. To search for images, the users type in a text query and the search engines rank the result images almost sorely based on the text meta-words. The abundant visual information in the images themselves is largely neglected. Recently, we have observed several new features released in the aforementioned image search engines, especially Microsoft Live image search, which are clearly based on the analysis of the visual content. We summarize some of these features, give insights about how they are designed, and motivate new content analysis based features for text based image search engines.