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30 March 2000 Visual target selection employing local-to-global strategies for support vector machines
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Abstract
In this paper, we propose a new measure of novelty detection for target selection in visual scenes. Our approach to the definition of novelty is based on the use of local kernels and Fisher information metric in the context of support vector machine regression. We discuss the applications in the specific context of visual saccades as a mechanism of search and discuss naturel generations of the approach in other contexts. We also propose natural regularization approaches arising from consideration of the problem that can be applied to learning machines including the SVM.
© (2000) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hamid Eghbalnia and Amir H. Assadi "Visual target selection employing local-to-global strategies for support vector machines", Proc. SPIE 4055, Applications and Science of Computational Intelligence III, (30 March 2000); https://doi.org/10.1117/12.380563
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