Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Query refinement for multimedia similarity retrieval in MARS
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
Comparing discriminating transformations and SVM for learning during multimedia retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Multispace KL for Pattern Representation and Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Association and Content-Based Retrieval
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
MKL-Tree: A Hierarchical Data Structure for Indexing Multidimensional Data
DEXA '02 Proceedings of the 13th International Conference on Database and Expert Systems Applications
A Relevance Feedback Architecture for Content-based Multimedia Information Retrieval Systems
CAIVL '97 Proceedings of the 1997 Workshop on Content-Based Access of Image and Video Libraries (CBAIVL '97)
Efficient Query Refinement for Image Retrieval
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Improving Retrieval Performance by Long-term Relevance Information
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Multi-class relevance feedback content-based image retrieval
Computer Vision and Image Understanding
QCluster: relevance feedback using adaptive clustering for content-based image retrieval
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
A New Approach for Relevance Feedback Through Positive and Negative Samples
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Content-based multimedia information retrieval: State of the art and challenges
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Analyzing user's behavior on a video database
MDM '05 Proceedings of the 6th international workshop on Multimedia data mining: mining integrated media and complex data
Improving image retrieval performance with negative relevance feedback
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
The Big Brother Database: Evaluating Face Recognition in Smart Home Environments
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Similarity Searches in Face Databases
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Incremental template updating for face recognition in home environments
Pattern Recognition
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Relevance feedback has recently emerged as a solution to the problem of improving the retrieval performance of an image retrieval system based on low-level information such as color, texture and shape features. Most of the relevance feedback approaches limit the utilization of the user's feedback to a single search session, performing a short-term learning. In this paper we present a novel approach for short and long term learning, based on the definition of an adaptive similarity metric and of a high level representation of the images. For short-term learning, the relevant and non-relevant information given by the user during the feedback process is employed to create a positive and a negative subspace of the feature space. For long-term learning, the feedback history of all the users is exploited to create and update a representation of the images which is adopted for improving retrieval performance and progressively reducing the semantic gap between low-level features and high-level semantic concepts. The experimental results prove that the proposed method outperforms many other state of art methods in the short-term learning, and demonstrate the efficacy of the representation adopted for the long-term learning.