Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Selective Sampling for Nearest Neighbor Classifiers
Machine Learning
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Semi-Supervised Active Learning Framework for Image Retrieval
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Object Categorization by Learned Universal Visual Dictionary
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Content-based multimedia information retrieval: State of the art and challenges
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Rapid and brief communication: Active learning for image retrieval with Co-SVM
Pattern Recognition
A nearest-neighbor approach to relevance feedback in content based image retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Features for image retrieval: an experimental comparison
Information Retrieval
Hierarchical sampling for active learning
Proceedings of the 25th international conference on Machine learning
Lire: lucene image retrieval: an extensible java CBIR library
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Semisupervised SVM batch mode active learning with applications to image retrieval
ACM Transactions on Information Systems (TOIS)
Interactive Image Search by Color Map
ACM Transactions on Intelligent Systems and Technology (TIST)
Coached active learning for interactive video search
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Active Learning Methods for Interactive Image Retrieval
IEEE Transactions on Image Processing
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In this paper, the Relevance Feedback procedure for Content Based Image Retrieval is considered as an Exploration-Exploitation approach. The proposed method exploits the information obtained from the relevance score as computed by a Nearest Neighbor approach in the exploitation step. The idea behind the Nearest Neighbor relevance feedback is to retrieve the immediate neighborhood of the area of the feature space where relevant images are found. The exploitation step aims at returning to the user the maximum number of relevant images in a local region of the feature space. On the other hand, the exploration step aims at driving the search towards different areas of the feature space in order to discover not only relevant images but also informative images. Similar ideas have been proposed with Support Vector Machines, where the choice of the informative images has been driven by the closeness to the decision boundary. Here, we propose a rather simple method to explore the representation space in order to present to the user a wider variety of images. Reported results show that the proposed technique allows to improve the performance in terms of average precision and that the improvements are higher if compared to techniques that use an SVM approach.