The nature of statistical learning theory
The nature of statistical learning theory
VisualSEEk: a fully automated content-based image query system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
NeTra: a toolbox for navigating large image databases
Multimedia Systems - Special issue on video content based retrieval
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
Modern Information Retrieval
Relevance Feedback Decision Trees in Content-Based Image Retrieval
CBAIVL '00 Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'00)
Incremental semi-supervised subspace learning for image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Learning an image manifold for retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Multimodal concept-dependent active learning for image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
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
Enhancing relevance feedback in image retrieval using unlabeled data
ACM Transactions on Information Systems (TOIS)
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Graph embedding with constraints
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Laplacian regularized D-optimal design for active learning and its application to image retrieval
IEEE Transactions on Image Processing
Learning distance functions for image retrieval
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
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In content-based image retrieval (CBIR), relevance feedback has been proven to be a powerful tool for bridging the gap between low level visual features and high level semantic concepts. Traditionally, relevance feedback driven CBIR is often considered as a supervised learning problem where the user provided feedbacks are used to learn a distance metric or classification function. However, CBIR is intrinsically a semi-supervised learning problem in which the testing samples (images in the database) are present during the learning process. Moreover, when there are no sufficient feedbacks, these methods may suffer from the overfitting problem. In this paper, we propose a novel neighborhood preserving regression algorithm which makes efficient use of both labeled and unlabeled images. By using the unlabeled images, the geometrical structure of the image space can be incorporated into the learning system through a regularizer. Specifically, from all the functions which minimize the empirical loss on the labeled images, we select the one which best preserves the local neighborhood structure of the image space. In this way, our method can obtain a regression function which respects both semantic and geometrical structures of the image database. We present experimental evidence suggesting that our algorithm is able to use unlabeled data effectively for image retrieval.