Matrix computations (3rd ed.)
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth 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)
Active learning via transductive experimental design
ICML '06 Proceedings of the 23rd international conference on Machine learning
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Laplacian optimal design for image retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Graph-based semi-supervised learning with multiple labels
Journal of Visual Communication and Image Representation
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Convex experimental design using manifold structure for image retrieval
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Ranking with local regression and global alignment for cross media retrieval
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Unified video annotation via multigraph learning
IEEE Transactions on Circuits and Systems for Video Technology
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video Retrieval
Beyond distance measurement: constructing neighborhood similarity for video annotation
IEEE Transactions on Multimedia - Special section on communities and media computing
Laplacian regularized D-optimal design for active learning and its application to image retrieval
IEEE Transactions on Image Processing
Semi-Supervised Learning
Active learning in multimedia annotation and retrieval: A survey
ACM Transactions on Intelligent Systems and Technology (TIST)
Towards a Relevant and Diverse Search of Social Images
IEEE Transactions on Multimedia
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
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Content Based Image Retrieval (CBIR) has attracted increasing attention from both academia and industry. Relevance Feedback is one of the most effective techniques to bridge the semantic gap in CBIR. One of the key research problems related to relevance feedback is how to select the most informative images for users to label. In this paper, we propose a novel active learning algorithm, called Locally Regressive G-Optimal Design (LRGOD) for relevance feedback image retrieval. Our assumption is that for each image, its label can be well estimated based on its neighbors via a locally regressive function. LRGOD algorithm is developed based on a locally regressive least squares model which makes use of the labeled and unlabeled images, as well as simultaneously exploits the local structure of each image. The images that can minimize the maximum prediction variance are selected as the most informative ones. We evaluated the proposed LRGOD approach on two real-world image corpus: Corel and NUS-WIDE-OBJECT [5] datasets, and compare it to three state-of-the-art active learning methods. The experimental results demonstrate the effectiveness of the proposed approach.