Fab: content-based, collaborative recommendation
Communications of the ACM
A unified framework for semantics and feature based relevance feedback in image retrieval systems
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Keyblock: an approach for content-based image retrieval
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
User Modeling and User-Adapted Interaction
MindReader: Querying Databases Through Multiple Examples
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Interactive Learning with a "Society of Models"
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Syskill & webert: Identifying interesting web sites
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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
Kernel-based distance metric learning for content-based image retrieval
Image and Vision Computing
Intra-dimensional feature diagnosticity in the Fuzzy Feature Contrast Model
Image and Vision Computing
User Modeling and User-Adapted Interaction
Personalized Multimedia Retrieval in CADAL Digital Library
PCM '08 Proceedings of the 9th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Efficient search with changing similarity measures on large multimedia datasets
MMM'07 Proceedings of the 13th International conference on Multimedia Modeling - Volume Part II
Region-based semantic similarity propagation for image retrieval
PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing
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Adaptation to the characteristics of specific images and the preferences of individual users is critical to the success of an image retrieval system but insufficiently addressed by the existing approaches. In this paper, we propose an elegant and effective approach to data-adaptive and user-adaptive image retrieval based on the idea of peer indexing—describing an image through semantically relevant peer images. Specifically, we associate each image with a two-level peer index that models the “data characteristics” of the image as well as the “user characteristics” of individual users with respect to this image. Based on two-level image peer indexes, a set of retrieval parameters including query vectors and similarity metric are optimized towards both data and user characteristics by applying the pseudo feedback strategy. A cooperative framework is proposed under which peer indexes and image visual features are integrated to facilitate data- and user-adaptive image retrieval. Simulation experiments conducted on real-world images have verified the effectiveness of our approach in a relatively restricted setting.