Decision estimation and classification: an introduction to pattern recognition and related topics
Decision estimation and classification: an introduction to pattern recognition and related topics
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
ACM Computing Surveys (CSUR)
Learning and inferring a semantic space from user's relevance feedback for image retrieval
Proceedings of the tenth ACM international conference on Multimedia
Introduction to MPEG-7: Multimedia Content Description Interface
Introduction to MPEG-7: Multimedia Content Description Interface
Wavelet Based Texture Classification
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
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
Integrating Relevance Feedback Techniques for Image Retrieval Using Reinforcement Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semantic manifold learning for image retrieval
Proceedings of the 13th annual ACM international conference on Multimedia
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
Human-computer intelligent interaction: a survey
HCI'07 Proceedings of the 2007 IEEE international conference on Human-computer interaction
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In this paper we take a look at the predominant form of human computer interaction as used in image retrieval, called interactive search, and discuss a new approach called artificial imagination. This approach addresses two of the grand challenges in this field as identified by the research community: reducing the amount of iterations before the user is satisfied and the small sample problem. Artificial imagination will deepen the level of interaction with the user by giving the computer the ability to think along by synthesizing ('imagining') example images that ideally match all or parts of the picture the user has in mind. We discuss two methods of how to synthesize new images, of which the evolutionary synthesis approach receives our main focus.