An efficient modification of generalized gradient vector flow using directional contrast for salient object detection and intelligent scene analysis

Abstract

In the field of computer vision, scene analysis is a very important area of study. To analyze the scene present in an image, in this paper, the attempt is to enhance salient object information with background information. For this, the Generalized Gradient Vector Flow model is modified by adding contrast information. The contrast information of an image is obtained by computing the Minimum Directional Contrast of the image. Using Minimum Directional Contrast as a determinant of the salient object arises from the fact that the salient objects have higher Minimum Directional Contrast than the non-salient objects. The Minimum Directional Contrast information is added to the data term of Generalized Gradient Vector Flow so that for producing contours, not only edge information is utilized, but saliency information is also used. The result gives us the salient object and added relevant background information. The algorithm is tested on three public datasets. The evaluation is done based on precision, recall, accuracy, and F1-score after comparing with six state-of-the-art methods. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.