Superpixel-based salient region detection using the wavelet transform
Masoumeh Rezaei Abkenar, M. Omair Ahmad · 2016
Salient regions are the most dominant parts of an image, which capture human visual system's attention. Finding computational methods that are able to detect salient regions especially in images with messy background is a challenging task. In this paper, a novel segment-based saliency detection method using the wavelet transform is proposed. The human beings are attracted by objects or regions rather than individual pixels. Moreover, at pixel grid, a sudden change in a pixel from a cluttered scene obtains a high saliency value, whereas at segment grid, the saliency is determined by considering each pixel and its neighboring pixels. Thus, applying fast and efficient frequency transforms at the segment grid can improve the capability of the method in images with cluttered background or repeating distractors. The proposed method is evaluated on several images from a publicly available dataset of natural images. Experimental results show that the proposed method provides larger values of area under the receiver operating characteristic curve, precision-recall, and F-measure in comparison to some of the state-of-the-art methods.