Adaptive saliency fusion based on quality assessment

作者：Zhou, Xiaofei; Liu, Zhi^*; Sun, Guangling; Wang, Xiangyang

来源：Multimedia Tools and Applications, 2017, 76(22): 23187-23211.

DOI：10.1007/s11042-016-4093-8

• Saliency model
• Adaptive fusion
• Quality assessment
• Boosting

A variety of saliency models based on different schemes and methods have been proposed in the recent years, and the performance of these models often vary with images and complement each other. Therefore it is a natural idea whether we can elevate saliency detection performance by fusing different saliency models. This paper proposes a novel and general framework to adaptively fuse saliency maps generated using various saliency models based on quality assessment of these saliency maps. Given an input image and its multiple saliency maps, the quality features based on the input image and saliency maps are extracted. Then, a quality assessment model, which is learned using the boosting algorithm with multiple kernels, indicates the quality score of each saliency map. Next, a linear summation method with power-law transformation is exploited to fuse these saliency maps adaptively according to their quality scores. Finally, a graph cut based refinement method is exploited to enhance the spatial coherence of saliency and generate the high-quality final saliency map. Experimental results on three public benchmark datasets with state-of-the-art saliency models demonstrate that our saliency fusion framework consistently outperforms all saliency models and other fusion methods, and effectively elevates saliency detection performance.

• 出版日期2017-11
• 单位上海大学