Diffusions for global optimizations
SIAM Journal on Control and Optimization
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance evaluation in content-based image retrieval: overview and proposals
Pattern Recognition Letters - Special issue on image/video indexing and retrieval
Optimal Linear Representations of Images for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Content-based multimedia information retrieval: State of the art and challenges
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
The state of the art in image and video retrieval
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
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This paper presents an MCMC stochastic gradient algorithm for finding representations with optimal retrieval performance on given image datasets. For linear subspaces in the image space and the spectral space, the problem is formulated as that of optimization on a Grassmann manifold. By exploiting the underlying geometry of the manifold, a computationally effective algorithm is developed. The feasibility and effectiveness of the proposed algorithm are demonstrated through extensive experimental results.