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Salient region detection using feature extraction in the non-subsampled contourlet domain

The human visual system is attracted to the most dominant part of the image which is called salient region. There has been a surge of interest in the past few years to efficiently detect the salient regions of images. In this study, a new salient region detection method is proposed using the non-subsampled contourlet transform. It is known that this transform is capable of providing a multiscale, multi-directional and translation invariant decomposition of images. The proposed saliency detection method is realised by extracting various local and global features from the non-subsampled contourlet coefficients of the colour channels. A saliency map is obtained based on a linear combination of the local features and the distribution of the global features. In order to provide a better preservation of the structure and boundary of the objects and to obtain a more uniformly highlighted salient region, the saliency map is abstracted using an optimisation framework. Several experiments are conducted on sets of natural images to evaluate the performance of the proposed method. The results show that the performance of the proposed method is superior to that of the other existing methods in terms of precision-recall performance, F
-measure, and mean absolute error values.