Salient object detection is a challenging task in the computer vision due to complexity of images and insufficiency of a single feature for saliency map generation. The performance of saliency detection depends upon the manner in which these visual features are combined. In this paper, we propose an efficient features combination model in which different features are combined in an appropriate manner. To meet this objective, a metaheuristic optimization algorithm called teacher-learning-based optimization (TLBO) is employed to find an optimal weight vector for feature combination. TLBO is a parameterless, efficient, and robust algorithm as it does not require parameter tuning during its implementation. Furthermore, the optimization criteria of TLBO is modified using a novel fitness function for improved performance. To check the performance of the proposed model, we have conducted extensive experiments over six benchmark datasets and compared the results against seven state-of-the-art methods.

由于图像的复杂性和显着性图生成的单个功能不足，在计算机视觉中，显着的对象检测是一项艰巨的任务。显着性检测的性能取决于这些视觉特征的组合方式。在本文中，我们提出了一种有效的特征组合模型，其中以适当的方式组合了不同的特征。为了实现这一目标，采用了一种基于启发式优化的算法，称为基于教师学习的优化（TLBO），以找到用于特征组合的最优权向量。TLBO是一种无参数，高效且健壮的算法，因为它在实施过程中不需要参数调整。此外，使用新的适应度函数修改了TLBO的优化标准，以提高性能。