Making large-scale support vector machine learning practical
Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
A statistical study of on-line learning
On-line learning in neural networks
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Solving large scale linear prediction problems using stochastic gradient descent algorithms
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
Learning to rank: from pairwise approach to listwise approach
Proceedings of the 24th international conference on Machine learning
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
Proceedings of the 24th international conference on Machine learning
Accurate Image Search Using the Contextual Dissimilarity Measure
IEEE Transactions on Pattern Analysis and Machine Intelligence
An effective method for color image retrieval based on texture
Computer Standards & Interfaces
Hello neighbor: Accurate object retrieval with k-reciprocal nearest neighbors
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Query specific fusion for image retrieval
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Stochastic dual coordinate ascent methods for regularized loss
The Journal of Machine Learning Research
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Learning to rank has received great attentions in the field of text retrieval for several years. However, a few researchers introduce the topic into visual reranking due to the special nature of image presentation. In this paper, a novel unsupervised visual reranking is proposed, termed rank via the convolutional neural networks (RankCNN). This approach integrates deep learning with pseudo preference feedback. The optimal set of pseudo preference pairs is first detected from initial list by a modified graph-based method. Ranking is then reduced to pairwise classification in the architecture of CNN. In addition, Accelerated Mini-Batch Stochastic Dual Coordinate Ascent (ASDCA) is introduced to the framework to accelerate the training. The experiments indicate the competitive performance on the LETOR 4.0, the Paris and the Francelandmark dataset.