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
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Discriminant Embedding and Its Variants
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Semantic manifold learning for image retrieval
Proceedings of the 13th annual ACM international conference on Multimedia
A Unified Log-Based Relevance Feedback Scheme for Image Retrieval
IEEE Transactions on Knowledge and Data Engineering
Learning Distance Metrics with Contextual Constraints for Image Retrieval
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Learning image manifolds by semantic subspace projection
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Spectral regression: a unified subspace learning framework for content-based image retrieval
Proceedings of the 15th international conference on Multimedia
Semi-Supervised Learning
Similarity-based online feature selection in content-based image retrieval
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
iScope: personalized multi-modality image search for mobile devices
Proceedings of the 7th international conference on Mobile systems, applications, and services
Personalized multi-modality image management and search for mobile devices
Personal and Ubiquitous Computing
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To narrow the semantic gap in content-based image retrieval (CBIR), relevance feedback is utilized to explore knowledge about the user's intention in finding a target image or a image category. Users provide feedback by marking images returned in response to a query image as relevant or irrelevant. Existing research explores such feedback to refine querying process, select features, or learn a image classifier. However, the vast amount of unlabeled images is ignored and often substantially limited examples are engaged into learning. In this paper, we address the two issues and propose a novel effective method called Relevance Aggregation Projections (RAP) for learning potent subspace projections in a semi-supervised way. Given relevances and irrelevances specified in the feedback, RAP produces a subspace within which the relevant examples are aggregated into a single point and the irrelevant examples are simultaneously separated by a large margin. Regarding the query plus its feedback samples as labeled data and the remainder as unlabeled data, RAP falls in a special paradigm of imbalanced semi-supervised learning. Through coupling the idea of relevance aggregation with semi-supervised learning, we formulate a constrained quadratic optimization problem to learn the subspace projections which entail semantic mining and therefore make the underlying CBIR system respond to the user's interest accurately and promptly. Experiments conducted over a large generic image database show that our subspace approach outperforms existing subspace methods for CBIR even with few iterations of user feedback.