Searching Multimedia Databases by Content
Searching Multimedia Databases by Content
K-means clustering via principal component analysis
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Orthogonal locality preserving indexing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Learning a Mahalanobis Metric from Equivalence Constraints
The Journal of Machine Learning Research
Semi-supervised nonlinear dimensionality reduction
ICML '06 Proceedings of the 23rd international conference on Machine learning
Local and Global Structures Preserving Projection
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 02
Unsupervised learning of image manifolds by semidefinite programming
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
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Image feature space is typically complex due to the high dimensionality of data. Effective handling of this space has prompted many research efforts in the study of dimensionality reduction in the image domain. In this paper, we propose a semi-supervised reduction method that leverages relevance feedback information in the retrieval process to learn suitable linear and orthogonal embeddings. In the reduced space constructed by the proposed embedding, relevant images are kept close to each other, while irrelevant ones are dispersed far apart. The experimental results demonstrate the superiority of our method.