Hao Cheng
ABSTRACT
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.
- A. Bar-Hillel, T. Hertz, N. Shetal, and D. Weinshall. Learning a Mahalanobis Metric from Equivalence Constraints. Journal of Machine Learning Research, 6(2005), pp. 937--965. Google Scholar
Digital Library
- D. Cai, and X. F. He. Orthogonal Locality Preserving Projection. In Proc. of the 28th Intl. ACM SIGIR Conf. on Research and Development in information Retrieval. SIGIR '05. Google ScholarDigital Library
- L Cayton. Algorithm for Manifold Learning. Technical Report, 2005.Google Scholar
- H. Cheng, K. Hua, and K. Vu. Local and Global Structures Preserving Projection. In IEEE International Conference on Tools with Artificial Intelligence, 2007. ICTAI '07. Google ScholarDigital Library
- H. Cheng, K. Hua, and K. Vu. Semi-Supervised Dimensionality Reduction in Image Feature Space. Technical Report, University of Central Florida, 2007.Google Scholar
- C. Ding, and X. He. K-Means Clustering via Principal Component Analysis. In Proc. of the 21st Intl. Conf. on Machine Learning, 2004. ICML '04. Google ScholarDigital Library
- T. Deselaers, D. Keysers, and H. Ney. Features for Image Retrieval -- a Quantitative Comparison. in DAGM 2004, Pattern Recognition, 26th DAGM Symposium, 2004.Google ScholarCross Ref
- C. Faloutsos. Searching Multimedia Databases by Content. Kluwer Academic Publishers, 1996. Google ScholarDigital Library
- X. F. He, and P. Niyogi. Locality Preserving Projections. Neural Information Processing Systems, 2003. NIPS '03.Google Scholar
- X. M. Huo, X. L. Ni and A. K. Smith. A Survey of Manifold-based Learning Methods. In Mining of Enterprise Data, Eds. T. W. Liao and E. Triantaphyllou. World Scientific, 2007.Google Scholar
- Y. Rui, T. S. Huang, and S. Mehrotra. Relevance Feedback Techniques in Interactive Content-based Image Retrieval. IEEE Trans. On Circuits and Systems for Video Technology, vol. 8, pp 644--655. 1998. Google ScholarDigital Library
- K. Q. Weinberger, and L. K. Saul. Unsupervised Learning of Image Manifolds by Semidefinite Programming. In Proc. of 2004 Conf on Computer Vision and Pattern Recognition. CVPR '04 Google ScholarDigital Library
- M. Welling. Fisher Linear Discriminant Analysis. Technical Report, 2005.Google Scholar
- E. Xing, A. Y. Ng, M. Jordan, and S. Russell. Distance Metric Learning, with Application to Clustering with Side-Information. Neural Information Processing Systems, 2003. NIPS '03.Google Scholar
- X. Yang, H. Fu, H. Zha and J. Barlow. Semi-Supervised Nonlinear Dimensionality Reduction. In Proc. of the 23rd Intl. Conf. on Machine Learning, 2006. ICML '06. Google ScholarDigital Library
- D. Zhang, Z. H. Zhou, and S. Chen. Semi-Supervised Dimensionality Reduction. In Proc. of the 2007 SIAM Intl. Conf. on Data Mining. SDM '07.Google Scholar
- X. Zhu. Semi-Supervised Learning Literature Survey. Technical Report 1530, Department of Computer Sciences, University of Wisconsin at Madison, 2006. http://www.cs.wisc.edu/~jerryzhu/pub/ssl_survey.pdfGoogle Scholar
Index Terms
- Semi-supervised dimensionality reduction in image feature space
Recommendations
A unified framework for semi-supervised dimensionality reduction
In practice, many applications require a dimensionality reduction method to deal with the partially labeled problem. In this paper, we propose a semi-supervised dimensionality reduction framework, which can efficiently handle the unlabeled data. Under ...
Semi-supervised dimensionality reduction of hyperspectral imagery using pseudo-labels
A new semi-supervised feature reduction approach is proposed.Pseudo-labels are generated using Dirichlet Process Mixing Model, which are then used for learning a semi-supervised dimensionality reduction projection that simultaneously preserves ...
Local Weighted Semi-supervised Discriminant Analysis for Dimensionality Reduction
IHMSC '15: Proceedings of the 2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics - Volume 01In this paper, we present a novel weighted version of semi-supervised discriminant analysis method by assigning weights to each labeled samples. The proposed within-class weight can detect the outliers and between-class weight can discover the support ...
Comments