SOFT: salient object detection based on feature combination using teaching-learning-based optimization

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.

Automated summary

This paper proposes an efficient features combination model using a teacher-learning-based optimization algorithm, which is demonstrated to outperform seven state-of-the-art methods on six benchmark datasets.