Abstract

In this paper, we present a method for ob ject of interest detection. This method is statistical in nature and hinges in a model which combines salient features using a mixture of linear support vec- tor 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 combina- tion parameters for each of the salient features at hand. Thus, the method learns the combination of salient features such that a mixture of classi- fiers can be used to recover ob jects of interest in the image. We illustrate the utility of the method by applying our algorithm to the MSRA Salient Ob ject Database.