Improved Deer Hunting Optimization Algorithm for Videobased Salient Object Detection

Abstract

Recent days, many researchers have concentrated on the development in salient object detection which is important in numerous computer vision applications. Nevertheless, the main confront is the competent SOD employing the still images. Hence, this work presents the SOD method employing the developed Improved-Deer Hunting Optimization (Improved-DHO) approach. The approach experiences 3 steps that include saliency mapping, keyframe extraction, contourlet mapping, subsequently, a combination of attained outputs by exploiting the optimal coefficients. Initially, extracted frames are given to saliency as well as contourlet mapping concurrently to decide each pixel quality. Subsequently, the outcomes attained from the contourlet mapping and saliency mapping are combined by exploiting the random coefficients in order to attain the last consequence which is used to recognize the salient objects. Moreover, the developed Improved-DHO is used to select the optimal coefficients in order to detect salient objects. The investigational analysis of developed Improved-DHO based on the performance measures exposes that the developed Improved-DHO obtained a maximum accuracy, specificity, and sensitivity.

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