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
Incremental semi-supervised subspace learning for image 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
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Semantic manifold learning for image retrieval
Proceedings of the 13th annual ACM international conference on Multimedia
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
Active learning with statistical models
Journal of Artificial Intelligence Research
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
trNon-greedy active learning for text categorization using convex ansductive experimental design
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Locality condensation: a new dimensionality reduction method for image retrieval
MM '08 Proceedings of the 16th 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
Laplacian regularized D-optimal design for active learning and its application to image retrieval
IEEE Transactions on Image Processing
SED: supervised experimental design and its application to text classification
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Locally regressive G-optimal design for image retrieval
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Search web images using objects, backgrounds and conditions
Proceedings of the 20th ACM international conference on Multimedia
Active hashing and its application to image and text retrieval
Data Mining and Knowledge Discovery
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Relevance feedback is a powerful technique to enhance Content-Based Image Retrieval (CBIR) performance. It solicits the user's relevance judgments on the retrieved images returned by the CBIR systems. The user's labeling is then used to learn a classifier to distinguish between relevant and irrelevant images. However, the top returnedimages may not be the most informative ones. The challenge is thus to determine which unlabeled images would be the most informative (i.e., improve the classifier the most) if they were labeled and used as training samples. In this paper, we propose a novel active learning algorithm, called Laplacian Optimal Design (LOD), for relevance feedback image retrieval. Our algorithm is based on aregression model which minimizes the least square error on the measured (or, labeled) images and simultaneously preserves the local geometrical structure of the image space. Specifically, we assume that if two images are sufficiently close to each other, then their measurements (or, labels) are close as well. By constructing a nearest neighbor graph, the geometrical structure of the image space can be described by the graph Laplacian. We discuss how results from the field of optimal experimental design may be used to guide our selection of a subset of images, which gives us the most amount of information. Experimental results on Corel database suggest that theproposed approach achieves higher precision in relevance feedback image retrieval.