Object of Interest Detection by Saliency Learning
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...[Show more]
|Collections||ANU Research Publications|
|Source:||Proceedings of the European Conference on Computer Vision (ECCV 2010)|
|01_Khuwuthyakorn_Object_of_Interest_Detection_2010.pdf||759.12 kB||Adobe PDF||Request a copy|
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