Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Theory of keyblock-based image retrieval
ACM Transactions on Information Systems (TOIS)
Learning-based linguistic indexing of pictures with 2--d MHMMs
Proceedings of the tenth ACM international conference on Multimedia
On the Surprising Behavior of Distance Metrics in High Dimensional Spaces
ICDT '01 Proceedings of the 8th International Conference on Database Theory
A data-driven reflectance model
ACM SIGGRAPH 2003 Papers
Learning a Locality Preserving Subspace for Visual Recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Locality preserving indexing for document representation
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Learning an image manifold for retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Discrete visual features modeling via leave-one-out likelihood estimation and applications
Journal of Visual Communication and Image Representation
CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines
IEEE Transactions on Circuits and Systems for Video Technology
Learning a semantic space from user's relevance feedback for image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
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In recent years, the spatial resolution of a remote sensing image becomes much higher than ten years ago. The research of image processing and analyzing based on traditional low resolution image has already not satisfied the need for getting more accurate information. Identifying particular objects from remote sensing image become more important to Digital City and real-time monitoring. The paper proposes a novel semantic manifold interpretation method of high-resolution sensor image, which uses semantics associated with ground object images to improve object recognition works. Our approach first learns the multiple semantic classes by using a semi-supervised manifold learning algorithm to produce a "semantic manifold" of the ground object, and then the RF(Relevance Feedback) iteration based on manifold ranking algorithm is then run on the semantic manifold. The methods are applied to several high-resolution example images, and some buildings as test objects in images are recognized. Those examples illuminate that the method proposed in this paper is effective and accurate, especially for multi-view, multi-spectral, all-weather remote images.