Saliency detection using adaptive background template

Affiliation of Author(s):计算机科学与技术学院/人工智能学院/软件学院

Journal:IET COMPUTER VISION

Key Words:REGION DETECTION OBJECT DETECTION SUPERPIXELS MODEL

Abstract:Since most existing saliency detection models are not suitable for the condition that the salient objects are near at the image border, the authors propose a saliency detection approach based on adaptive background template (SCB) despite of the position of the salient objects. First, a selection strategy is presented to establish the adaptive background template by removing the potential saliency superpixels from the image border regions, and the initial saliency map is obtained. Second, a propagation mechanism based on K-means algorithm is designed for maintaining the neighbourhood coherence of the above saliency map. Finally, a new spatial prior is presented to integrate the saliency detection results by aggregating two complementary measures such as image centre preference and the background template exclusion. Comprehensive evaluations on six benchmark datasets indicate that the authors' method outperforms other state-of-the-art approaches. In addition, a new dataset containing 300 challenging images is constructed for evaluating the performance of various salient object detection methods.

ISSN No.:1751-9632

Translation or Not:no

Date of Publication:2017-09-01

Co-author:Lin Huafeng,Zhou Peiyun,Liang Dachuan,Li Dongmin

Correspondence Author:李静,lj

Date of Publication:2017-09-01