A Database for Handwritten Text Recognition Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental semi-supervised subspace learning for image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Semantic manifold learning for image retrieval
Proceedings of the 13th annual ACM international conference on Multimedia
Maximum unfolded embedding: formulation, solution, and application for image clustering
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Spectral regression: a unified subspace learning framework for content-based image retrieval
Proceedings of the 15th international conference on Multimedia
LS-MMRM '09 Proceedings of the First ACM workshop on Large-scale multimedia retrieval and mining
Kernel Discriminant Learning for Ordinal Regression
IEEE Transactions on Knowledge and Data Engineering
Supervised manifold learning for image and video classification
Proceedings of the international conference on Multimedia
Image ranking and retrieval based on multi-attribute queries
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Neighborhood preserving ordinal regression
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
Ordinal regularized manifold feature extraction for image ranking
Signal Processing
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In this paper, we present a novel algorithm called manifold ordinal regression (MOR) for image ranking. By modeling the manifold information in the objective function, MOR is capable of uncovering the intrinsically nonlinear structure held by the image data sets. By optimizing the ranking information of the training data sets, the proposed algorithm provides faithful rating to the new coming images. To offer more general solution for the real-word tasks, we further provide the semi-supervised manifold ordinal regression (SS-MOR). Experiments on various data sets validate the effectiveness of the proposed algorithms.