Modification of Gradient Vector Flow Using Directional Contrast for Salient Object Detection

Abstract

Scene analysis is a relevant research field for its several applications in the area of computer vision. This paper attempts to analyze scene information present in the image by augmenting salient object information with background information. The salient object is initially identified using a method called as Minimum Directional Contrast (MDC). The underlying assumption behind using this method for defining salient objects is that salient pixels have higher minimum directional contrast than nonsalient pixels. Finding MDC provides us with a raw salient metric. The gradient vector flow (GVF) model of image segmentation inculcates the raw saliency information. The gradient of MDC is calculated and added to the data term of the energy functional of GVF so that the contour formation utilizes not only edge formation but also saliency information. The result obtained gives us not only the salient object but also added background information. Three public datasets have been used to evaluate the results obtained. The comparative study of the proposed method for salient object detection with other state-of-the-art methods available in the literature is presented in terms of precision, recall, and F1-Score.