A convex hull approach in conjunction with Gaussian mixture model for salient object detection

The capability of humans in distinguishing salient objects from background is at par excellence. The researchers are yet to develop a model that matches the detection accuracy as well as computation time taken by the humans. In this paper we attempted to improve the detection accuracy without capitalizing much of computation time. The model utilizes the fact that maximal amount of information is present at the corners and edges of an object in the image. Firstly the keypoints are extracted from the image by using multi-scale Harris and multi-scale Gabor functions. Then the image is roughly segmented into two regions: a salient region and a background region, by constructing a convex hull over these keypoints. Finally the pixels of the two regions are considered as samples to be drawn from a multivariate kernel function whose parameters are estimated using expectation maximization algorithm, to yield a saliency map. The performance of the proposed model is evaluated in terms of precision, recall, F-measure, area under curve and computation time using six publicly available image datasets. Experimental results demonstrate that the proposed model outperformed the existing state-of-the-art methods in terms of recall, F-measure and area under curve on all the six datasets, and precision on four datasets. The proposed method also takes comparatively less computation time in comparison to many existing methods. (C) 2016 Elsevier Inc. All rights reserved.