Unsupervised Feature Selection for Salient Object Detection

Feature selection plays a crucial role in deciding the salient regions of an image as in any other pattern recognition problem. However the problem of identifying the relevant features that plays a fundamental role in saliency of an image has not received much attention so far. We introduce an unsupervised feature selection method to improve the accuracy of salient object detection. The noisy irrelevant features in the image are identified by maximizing the mixing rate of a Markov process running on a linear combination of various graphs, each representing a feature. The global optimum of this convex problem is achieved by maximizing the second smallest eigen value of the graph Laplacian via semi-definite programming. The enhanced image graph model, after the removal of irrelevant features, is shown to improve the salient object detection performance on a large image data base with annotated 'ground truth'.