Elements of information theory
Elements of information theory
A general language model for information retrieval
Proceedings of the eighth international conference on Information and knowledge management
Document language models, query models, and risk minimization for information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Model-based feedback in the language modeling approach to information retrieval
Proceedings of the tenth international conference on Information and knowledge management
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Negative pseudo-relevance feedback in content-based video retrieval
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
A study of smoothing methods for language models applied to information retrieval
ACM Transactions on Information Systems (TOIS)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Modeling Scenes with Local Descriptors and Latent Aspects
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Hybrid visual and conceptual image representation within active relevance feedback context
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Effective and efficient object-based image retrieval using visual phrases
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
IDEAS '06 Proceedings of the 10th International Database Engineering and Applications Symposium
Near-duplicate keyframe retrieval with visual keywords and semantic context
Proceedings of the 6th ACM international conference on Image and video retrieval
Improve retrieval accuracy for difficult queries using negative feedback
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Object-based image retrieval with kernel on adjacency matrix and local combined features
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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We consider the problem of ranking refinement for image object retrieval, whose goal is to improve an existing ranking function by a small number of labeled instances. To retrieve the relevant image object, one state-of-the-art approach is to use the relevance feedback: it first ranks the images in database based on a given ranking function (i.e., base ranker), and then rerank the initial result by further introducing user's feedback information. The key challenge of combining the information from the base ranker and user's feedback comes from the fact that the base ranker tends to give an imperfect result and the information obtained from user's feedback tends to be very noisy. This paper describes an Intention-Focused Active Reranking, an approach for automatically finding the right information to re-estimate the query model. Three novel strategies are proposed to boost the performance of the base ranker: (1) an active selection criterion, which obtains a small number of feedback images that are the most informative to the base ranker for user labeling; (2) the user intention verification, which captures the user's intention in object level to alleviate the query drift problem; (3) a discriminative query model re-estimation, which augments the generative approach with a model of the discriminative information conveyed by positive and negative feedback information. Experiments on a real world data set demonstrate the effectiveness of the proposed approach and furthermore it significantly outperforms the baseline visual bag-of-words retrieval.