Focus prior estimation for salient object detection
Xiaoli Sun, Xiujun Zhang, Wenbin Zou, Chen Xu · 2017
In the past five years, salient object detection has become one of the hot topics in the field of computer vision. Focus is a naturally strong indicator for the salient object detection task, but is not well studied. In this paper, a novel method is proposed to estimate the focus prior map for an arbitrary image. Different from the current edge density estimation based methods, the proposed method is based on the sparse defocus dictionary learning on a newly designed dataset. The focus strength is measured by the number of non-zero coefficients of the dictionary atoms. Objectness proposal method is introduced to improve the performance. Comparison with the other focusness estimation methods, the proposed focus prior map is more accurate and easier to be integrated by the other salient object detection methods. Experiments have confirmed the effectiveness and importance of the proposed focus prior.