IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Video search reranking via information bottleneck principle
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
A reranking approach for context-based concept fusion in video indexing and retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Video search reranking through random walk over document-level context graph
Proceedings of the 15th international conference on Multimedia
Learning a Maximum Margin Subspace for Image Retrieval
IEEE Transactions on Knowledge and Data Engineering
VisualRank: Applying PageRank to Large-Scale Image Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semi-supervised kernel density estimation for video annotation
Computer Vision and Image Understanding
A Novel Method of Combined Feature Extraction for Recognition
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
CrowdReranking: exploring multiple search engines for visual search reranking
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Unified video annotation via multigraph learning
IEEE Transactions on Circuits and Systems for Video Technology
Beyond distance measurement: constructing neighborhood similarity for video annotation
IEEE Transactions on Multimedia - Special section on communities and media computing
Multiple view semi-supervised dimensionality reduction
Pattern Recognition
Active reranking for web image search
IEEE Transactions on Image Processing
Co-reranking by mutual reinforcement for image search
Proceedings of the ACM International Conference on Image and Video Retrieval
Multimodal Fusion for Video Search Reranking
IEEE Transactions on Knowledge and Data Engineering
SemiCCA: Efficient Semi-supervised Learning of Canonical Correlations
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Summarizing tourist destinations by mining user-generated travelogues and photos
Computer Vision and Image Understanding
Visual search reranking via adaptive particle swarm optimization
Pattern Recognition
Sparse canonical correlation analysis
Machine Learning
Semi-supervised kernel canonical correlation analysis with application to human fMRI
Pattern Recognition Letters
Beyond search: Event-driven summarization for web videos
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Diversifying the Image Relevance Reranking with Absorbing Random Walks
ICIG '11 Proceedings of the 2011 Sixth International Conference on Image and Graphics
Query-specific visual semantic spaces for web image re-ranking
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Towards a Relevant and Diverse Search of Social Images
IEEE Transactions on Multimedia
Facial expression recognition using kernel canonical correlation analysis (KCCA)
IEEE Transactions on Neural Networks
Complex Object Correspondence Construction in Two-Dimensional Animation
IEEE Transactions on Image Processing
Image Retagging Using Collaborative Tag Propagation
IEEE Transactions on Multimedia
Ordinal regularized manifold feature extraction for image ranking
Signal Processing
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Ranking relevance degree information is widely utilized in the ranking models of information retrieval applications, such as text and multimedia retrieval, question answering, and visual search reranking. However, existing feature dimensionality reduction methods neglect this kind of valuable potential supervised information. In this paper, we extend the pairwise constraints from the traditional class labels to ranking relevance degrees, and propose a novel dimensionality reduction method called Rank-CCA. Rank-CCA effectively incorporates ranking relevance constraints into standard canonical correlation analysis (CCA) algorithm, and is able to employ the knowledge of both unlabeled and labeled data. In the application of visual search reranking, our proposed method is verified through extensive experimental studies. Experimental results show that Rank-CCA is superior to standard CCA and semi-supervised CCA (Semi-CCA) algorithm, and achieves comparable performance with several state-of-the-art reranking methods while preserving the superiority of low dimensional features.