Object of interest detection by saliency learning

Authors:
Pattaraporn Khuwuthyakorn;Antonio Robles-Kelly;Jun Zhou
Affiliations:
RSISE, Australian National University, Canberra, ACT, Australia and Cooperative Research Centre for National Plant Biosecurity, Canberra, ACT, Australia;RSISE, Australian National University, Canberra, ACT, Australia and National ICT Australia, Canberra, ACT, Australia;RSISE, Australian National University, Canberra, ACT, Australia and National ICT Australia, Canberra, ACT, Australia
Venue:
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Year:
2010

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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.