Decision estimation and classification: an introduction to pattern recognition and related topics
Decision estimation and classification: an introduction to pattern recognition and related topics
Mix and match features in the ImageRover search engine
Principles of visual information retrieval
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Learning and inferring a semantic space from user's relevance feedback for image retrieval
Proceedings of the tenth ACM international conference on Multimedia
Strategies for Positive and Negative Relevance Feedback in Image Retrieval
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
Relevance feedback: perceptual learning and retrieval in bio-computing, photos, and video
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
ACM SIGMM retreat report on future directions in multimedia research
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Niching in evolution strategies
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Integrating Relevance Feedback Techniques for Image Retrieval Using Reinforcement Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-based multimedia information retrieval: State of the art and challenges
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Aspect-based relevance learning for image retrieval
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
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In this project (VIRSI) we investigate the promising content-based retrieval paradigm known as interactive search or relevance feedback, and aim to extend it through the use of synthetic imagery. In relevance feedback methods, the user himself is a key factor in the search process as he provides positive and negative feedback on the results, which the system uses to iteratively improve the set of candidate results. In our approach we closely integrate the generation of synthetic imagery in the relevance feedback process through a new fundamental paradigm: Artificial Imagination (AIm).