Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extraction of feature subspaces for content-based retrieval using relevance feedback
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Signal Processing - Special section on content-based image and video retrieval
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dimension Reduction for Supervised Ordering
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Learning to rank: from pairwise approach to listwise approach
Proceedings of the 24th international conference on Machine learning
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Query dependent ranking using K-nearest neighbor
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Geometric Mean for Subspace Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Document selection methodologies for efficient and effective learning-to-rank
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
An introduction to nonlinear dimensionality reduction by maximum variance unfolding
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Ranking model adaptation for domain-specific search
Proceedings of the 18th ACM conference on Information and knowledge management
Feature selection for ranking using boosted trees
Proceedings of the 18th ACM conference on Information and knowledge management
Learning to rank for content-based image retrieval
Proceedings of the international conference on Multimedia information retrieval
Hierarchical feature selection for ranking
Proceedings of the 19th international conference on World wide web
Unsupervised feature selection for multi-cluster data
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Supervised reranking for web image search
Proceedings of the international conference on Multimedia
LSA based multi-instance learning algorithm for image retrieval
Signal Processing
Semi-supervised manifold ordinal regression for image ranking
MM '11 Proceedings of the 19th ACM 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
IEEE Transactions on Multimedia
Ordinal preserving projection: a novel dimensionality reduction method for image ranking
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Non-Negative Patch Alignment Framework
IEEE Transactions on Neural Networks
Manifold Regularized Discriminative Nonnegative Matrix Factorization With Fast Gradient Descent
IEEE Transactions on Image Processing
| Hi-index | 0.08 |
This paper proposes a novel feature extraction algorithm specifically designed for learning to rank in image ranking. Different from the previous works, the proposed method not only targets at preserving the local manifold structure of data, but also keeps the ordinal information among different data blocks in the low-dimensional subspace, where a ranking model can be learned effectively and efficiently. We first define the ideal directions of preserving local manifold structure and ordinal information, respectively. Based on the two definitions, a unified model is built to leverage the two kinds of information, which is formulated as an optimization problem. The experiments are conducted on two public available data sets: the MSRA-MM image data set and the ''Web Queries'' image data set, and the experimental results demonstrate the power of the proposed method against the state-of-the-art methods.