Query refinement for multimedia similarity retrieval in MARS
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
Extended Boolean information retrieval
Communications of the ACM
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
MindReader: Querying Databases Through Multiple Examples
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Maintaining Unstructured Case Base
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
QCluster: relevance feedback using adaptive clustering for content-based image retrieval
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Proceedings of the 12th annual ACM international conference on Multimedia
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
A survey of content-based image retrieval with high-level semantics
Pattern Recognition
IEEE Transactions on Image Processing
Action recognition with exemplar based 2.5d graph matching
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
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Conventional approaches to relevance feedback in content-based image retrieval are based on the assumption that relevant images are physically close to the query image, or the query regions can be identified by a set of clustering centers. However, semantically related images are often scattered across the visual space. It is not always reliable that the refined query point or the clustering centers are capable of representing a complex query region.In this work, we propose a novel relevance feedback approach which directly aims at extracting a set of samples to represent the query region, regardless of its underlying shape. The sample set extracted by our method is competent as well as compact for subsequent retrieval. Moreover, we integrate feature re-weighting in the process to estimate the importance of each image descriptor. Unlike most existing relevance feedback approaches in which all query points share a same feature weight distribution, our method re-weights the feature importance for each relevant image respectively, so that the representative and discriminative ability for all the images can be maximized. Experimental results on two databases show the effectiveness of our approach.