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CVPR
2007
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Local Ensemble Kernel Learning for Object Category Recognition

9 years 5 months ago
Local Ensemble Kernel Learning for Object Category Recognition
This paper describes a local ensemble kernel learning technique to recognize/classify objects from a large number of diverse categories. Due to the possibly large intraclass feature variations, using only a single unified kernel-based classifier may not satisfactorily solve the problem. Our approach is to carry out the recognition task with adaptive ensemble kernel machines, each of which is derived from proper localization and regularization. Specifically, for each training sample, we learn a distinct ensemble kernel constructed in a way to give good classification performance for data falling within the corresponding neighborhood. We achieve this effect by aligning each ensemble kernel with a locally adapted target kernel, followed by smoothing out the discrepancies among kernels of nearby data. Our experimental results on various image databases manifest that the technique to optimize local ensemble kernels is effective and consistent for object recognition.
Yen-Yu Lin, Tyng-Luh Liu, Chiou-Shann Fuh
Added 02 Jun 2010
Updated 02 Jun 2010
Type Conference
Year 2007
Where CVPR
Authors Yen-Yu Lin, Tyng-Luh Liu, Chiou-Shann Fuh
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