自动化学报2016，Vol.42Issue(8)：1259-1273,15.DOI:10.16383/j.aas.2016.c150387
一种基于词袋模型的新的显著性目标检测方法
A Novel Salient Object Detection Method Using Bag-of-features
摘要
Abstract
A novel salient object detection algorithm via bag-of-features (BoF) is proposed. Specifically, it uses objectness to compute the prior saliency map. Then, BoF model is constructed in each superpixel and the conditional probabilities map is calculated. The prior and conditional probabilities saliency maps are finally fused by Bayes0 theorem. Extensive experiments against state-of-art methods are carried out on ASD, SED and SOD benchmark datasets. Experimental results show that the proposed method performs favorably against the sixteen state-of-art methods in terms of precision and recall, and highlights the salient ob jects more effectively.关键词
词袋模型/目标性/贝叶斯模型/视觉显著性/显著性目标检测Key words
Bag-of-features (BOF)/objective/Bayesian model/visual saliency/salient object detection引用本文复制引用
杨赛,赵春霞,徐威..一种基于词袋模型的新的显著性目标检测方法[J].自动化学报,2016,42(8):1259-1273,15.基金项目
Manuscript received June 23,2015 （）
accepted October 10,2015国家自然科学基金(61272220)资助 （61272220）
