VisualSEEk: a fully automated content-based image query system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
MindReader: Querying Databases Through Multiple Examples
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Blobworld: A System for Region-Based Image Indexing and Retrieval
VISUAL '99 Proceedings of the Third International Conference on Visual Information and Information Systems
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Real-Time Computerized Annotation of Pictures
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Statistical Framework for Image Category Search from a Mental Picture
IEEE Transactions on Pattern Analysis and Machine Intelligence
Experiments in mental face retrieval
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
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
HEAT: Iterative relevance feedback with one million images
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Content-based image retrieval systems have to cope with two different regimes: understanding broadly the categories of interest to the user, and refining the search in this or these categories to converge to specific images among them. Here, in contrast with other types of retrieval systems, these two regimes are of great importance since the search initialization is hardly optimal (i.e. the page-zero problem) and the relevance feedback must tolerate the semantic gap of the image's visual features. We present a new approach that encompasses these two regimes, and infers from the user actions a seamless transition between them. Starting from a query-free approach meant to solve the page-zero problem, we propose an adaptive exploration/exploitation trade-off that transforms the original framework into a versatile retrieval framework with full searching capabilities. Our approach is compared to the state-of-the-art it extends by conducting user evaluations on a collection of 60,000 images from the ImageNet database.