Salient object detection using background subtraction, Gabor filters, objectness and minimum directional backgroundness

Abstract

Salient object detection is the process of identifying essential objects in an image. This paper solves this problem using background subtraction, Gabor filters, minimum directional backgroundness, and objectness. The first step is to calculate a backgroundness score for each region by calculating the difference between the feature vector of image boundary and image regions. This backgroundness map is then used for calculating the minimum directional background difference. The image is segmented using Gabor filters, and then the objectness criterion is used to choose the segment containing the salient object. The normalized foreground saliency map is then used to refine the selected segment. Further enhancement of this intermediate output is done using morphological operations, and boundary correction is done using the method of lazy snapping. The algorithm is tested on eight publicly available datasets and is compared against five algorithms. The performance is evaluated by PR-curve, F-Measure curve, and Mean Absolute Error. © 2019 Elsevier Inc.