Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gray Scale and Rotation Invariant Texture Classification with Local Binary Patterns
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Understanding user goals in web search
Proceedings of the 13th international conference on World Wide Web
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Evaluating implicit measures to improve web search
ACM Transactions on Information Systems (TOIS)
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Improving web search ranking by incorporating user behavior information
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
ImprovingWeb-based Image Search via Content Based Clustering
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search
ACM Transactions on Information Systems (TOIS)
What are you looking for?: an eye-tracking study of information usage in web search
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Predicting clicks: estimating the click-through rate for new ads
Proceedings of the 16th international conference on World Wide Web
Random walks on the click graph
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
VisualRank: Applying PageRank to Large-Scale Image Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
How does clickthrough data reflect retrieval quality?
Proceedings of the 17th ACM conference on Information and knowledge management
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Tailoring click models to user goals
Proceedings of the 2009 workshop on Web Search Click Data
A dynamic bayesian network click model for web search ranking
Proceedings of the 18th international conference on World wide web
Query aware visual similarity propagation for image search reranking
MM '09 Proceedings of the 17th ACM international conference on Multimedia
A model to estimate intrinsic document relevance from the clickthrough logs of a web search engine
Proceedings of the third ACM international conference on Web search and data mining
Using clicks as implicit judgments: expectations versus observations
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Investigating the effectiveness of clickthrough data for document reordering
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Robust visual reranking via sparsity and ranking constraints
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Leveraging user comments for aesthetic aware image search reranking
Proceedings of the 21st international conference on World Wide Web
The impact of images on user clicks in product search
Proceedings of the Twelfth International Workshop on Multimedia Data Mining
Image ranking based on user browsing behavior
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Multimodal re-ranking of product image search results
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Content-Based re-ranking of text-based image search results
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
MLRank: Multi-correlation Learning to Rank for image annotation
Pattern Recognition
Click-boosting random walk for image search reranking
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
Annotation for free: video tagging by mining user search behavior
Proceedings of the 21st ACM international conference on Multimedia
Why not, WINE?: towards answering why-not questions in social image search
Proceedings of the 21st ACM international conference on Multimedia
Clickage: towards bridging semantic and intent gaps via mining click logs of search engines
Proceedings of the 21st ACM international conference on Multimedia
A heterogenous automatic feedback semi-supervised method for image reranking
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Image search by graph-based label propagation with image representation from DNN
Proceedings of the 21st ACM international conference on Multimedia
Multimedia search reranking: A literature survey
ACM Computing Surveys (CSUR)
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Our objective is to improve the performance of keyword based image search engines by re-ranking their original results. To this end, we address three limitations of existing search engines in this paper. First, there is no straight-forward, fully automated way of going from textual queries to visual features. Image search engines therefore primarily rely on static and textual features for ranking. Visual features are mainly used for secondary tasks such as finding similar images. Second, image rankers are trained on query-image pairs labeled with relevance judgments determined by human experts. Such labels are well known to be noisy due to various factors including ambiguous queries, unknown user intent and subjectivity in human judgments. This leads to learning a sub-optimal ranker. Finally, a static ranker is typically built to handle disparate user queries. The ranker is therefore unable to adapt its parameters to suit the query at hand which again leads to sub-optimal results. We demonstrate that all of these problems can be mitigated by employing a re-ranking algorithm that leverages aggregate user click data. We hypothesize that images clicked in response to a query are mostly relevant to the query. We therefore re-rank the original search results so as to promote images that are likely to be clicked to the top of the ranked list. Our re-ranking algorithm employs Gaussian Process regression to predict the normalized click count for each image, and combines it with the original ranking score. Our approach is shown to significantly boost the performance of the Bing image search engine on a wide range of tail queries.