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Object of Interest Detection by Saliency Learning

Khuwuthyakorn, Pattaraporn; Robles-Kelly, Antonio; Zhou, Jun


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]

CollectionsANU Research Publications
Date published: 2010
Type: Conference paper
Source: Proceedings of the European Conference on Computer Vision (ECCV 2010)
DOI: 10.1007/978-3-642-15552-9_46


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