Object of Interest Detection by Saliency Learning
Date
2010
Authors
Khuwuthyakorn, Pattaraporn
Robles-Kelly, Antonio
Zhou, Jun
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Springer
Abstract
In this paper, we present a method for object of interest detection. This method is statistical in nature and hinges in a model which combines salient features using a mixture of linear support vector machines. It exploits a divide-and-conquer strategy by partitioning the feature space into sub-regions of linearly separable data-points. This yields a structured learning approach where we learn a linear support vector machine for each region, the mixture weights, and the combination parameters for each of the salient features at hand. Thus, the method learns the combination of salient features such that a mixture of classifiers can be used to recover objects of interest in the image. We illustrate the utility of the method by applying our algorithm to the MSRA Salient Object Database.
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Source
Proceedings of the European Conference on Computer Vision (ECCV 2010)
Type
Conference paper
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2037-12-31