The nature of statistical learning theory
The nature of statistical learning theory
Iterative refinement by relevance feedback in content-based digital image retrieval
MULTIMEDIA '98 Proceedings of the sixth ACM international conference on Multimedia
Probabilistic feature relevance learining for content-based image retrieval
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integrated Browsing and Querying for Image Databases
IEEE MultiMedia
MindReader: Querying Databases Through Multiple Examples
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Content-based 3D Neuroradiologic Image Retrieval: Preliminary Results
CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
Training Templates for Scene Classification using a Few Examples
CAIVL '97 Proceedings of the 1997 Workshop on Content-Based Access of Image and Video Libraries (CBAIVL '97)
Relevance Feedback Decision Trees in Content-Based Image Retrieval
CBAIVL '00 Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'00)
Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Efficient Query Refinement for Image Retrieval
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Query Reformulation for Content Based Multimedia Retrieval in MARS
ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
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
Addressing CBIR efficiency, effectiveness, and retrieval subjectivity simultaneously
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
Support vector machines for region-based image retrieval
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Indexing pictorial documents by their content: a survey of current techniques
Image and Vision Computing
Random sampling based SVM for relevance feedback image retrieval
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Hidden semantic concept discovery in region based image retrieval
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Bootstrapping SVM active learning by incorporating unlabelled images for image retrieval
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
IEEE Transactions on Image Processing
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Ego-similarity measurement for relevance feedback
Expert Systems with Applications: An International Journal
Content-based attention ranking using visual and contextual attention model for baseball videos
IEEE Transactions on Multimedia - Special issue on integration of context and content
Relevance feature mapping for content-based image retrieval
Proceedings of the Tenth International Workshop on Multimedia Data Mining
Relevance feature mapping for content-based multimedia information retrieval
Pattern Recognition
Asymmetric bayesian learning for image retrieval with relevance feedback
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Machine Learning
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This paper is on user relevance for image retrieval. We take this problem as a standard two-class pattern classification problem aiming at refining the retrieval precision by learning through the user relevance feedback data. However, we have investigated the problem by noting two important unique characteristics of the problem: small sample collection and asymmetric sample distributions between positive and negative samples. We have developed a novel approach to empirical Bayesian learning to solve for this problem by explicitly exploiting the two unique characteristics, which is the methodology of Bayesian Learning in Asymmetric and Small sample collections, thus called BALAS. In BALAS different learning strategies are used for positive and negative sample collections, respectively, based on the two unique characteristics. By defining the relevancy confidence as the relevant posterior probability, we have developed an integrated ranking scheme in BALAS, which complementarily combines the subjective relevancy confidence and the objective similarity measure to capture the overall retrieval semantics. The experimental evaluations have confirmed the rationale of the proposed ranking scheme, and have also demonstrated that BALAS is superior to an existing relevance feedback method in the current literature in capturing the overall retrieval semantics. antics.