Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Gene functional classification from heterogeneous data
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Locality-sensitive hashing scheme based on p-stable distributions
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning the semantics of multimedia queries and concepts from a small number of examples
Proceedings of the 13th annual ACM international conference on Multimedia
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Pattern recognition with a Bayesian kernel combination machine
Pattern Recognition Letters
A New Multiple Kernel Approach for Visual Concept Learning
MMM '09 Proceedings of the 15th International Multimedia Modeling Conference on Advances in Multimedia Modeling
Incremental indexing and distributed image search using shared randomized vocabularies
Proceedings of the international conference on Multimedia information retrieval
Comparing compact codebooks for visual categorization
Computer Vision and Image Understanding
Per-sample multiple kernel approach for visual concept learning
Journal on Image and Video Processing - Special issue on selected papers from multimedia modeling conference 2009
Nonlinear Combination of Multiple Kernels for Support Vector Machines
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
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Multiple kernel learning (MKL) methods is widely used in object detection. The conventional MKL methods employ a linear and stationary kernel combination format which cannot accurately describe the distributions of complex data. This paper proposes an E2LSH based clustering algorithm which combines the advantages of nonlinear multiple kernel combination methods-E2LSH-MKL. E2LSH-MKL is a nonlinear and nonstationary multiple kernel learning method. This method utilizes the Hadamard product to realize nonlinear combination of multiple different kernels in order to make full use of information generated from the nonlinear interaction of different kernels. Besides, the method employs E2LSH-based clustering algorithm to group images into subsets, then assigns cluster-related kernel weights according to relative contributions of different kernels on each image subset to realize nonstationary weighting of multiple kernels to improve learning performance. Finally, E2LSH-MKL is applied to object detection. Experiment results on datasets of TRECVID 2005 and Caltech-256 show that our method is superior to the state-of-the-art multiple kernel learning methods.