Ten lectures on wavelets
Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
IRM: integrated region matching for image retrieval
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Semantic based image retrieval: a probabilistic approach
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
Does organisation by similarity assist image browsing?
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Digital libraries: Introduction
Communications of the ACM
Design and management of multimedia information systems
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Comparing discriminating transformations and SVM for learning during multimedia retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Supporting Ranked Boolean Similarity Queries in MARS
IEEE Transactions on Knowledge and Data Engineering
MindReader: Querying Databases Through Multiple Examples
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Adaptive Multi-class Metric Content-Based Image Retrieval
VISUAL '00 Proceedings of the 4th International Conference on Advances in Visual Information Systems
Bayesian Relevance Feedback for Content-Based Image Retrieval
CBAIVL '00 Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'00)
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)
Interactive Learning with a "Society of Models"
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
PicHunter: Bayesian Relevance Feedback for Image Retrieval
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
IEEE Transactions on Image Processing
Formulating distance functions via the kernel trick
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Pattern Recognition Methods for Querying and Browsing Technical Documentation
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
A few steps towards on-the-fly symbol recognition with relevance feedback
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
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Learning-enhanced relevance feedback is one of the most promising and active research directions in content-based image retrieval in recent years. However, the existing approaches either require prior knowledge of the data or converge slowly and are thus not coneffective. Motivated by the successful history of optimal adaptive filters, we present a new approach to interactive image retrieval based on an adaptive tree similarity model to solve these difficulties. The proposed tree model is a hierarchical nonlinear Boolean representation of a user query concept. Each path of the tree is a clustering pattern of the feedback samples, which is small enough and local in the feature space that it can be approximated by a linear model nicely. Because of the linearity, the parameters of the similartiy model are better learned by the optimal adaptive filter, which does not require any prior knowledge of the data and supports incremental learning with a fast convergence rate. The proposed approach is simple to implement and achieves better performance than most approaches. To illustrate the performance of the proposed approach, extensive experiments have been carded out on a large heterogeneous image collection with 17,000 images, which render promising results on a wide variety of queries.