计算机与现代化Issue(1)：61-64,70,5.DOI:10.3969/j.issn.1006-2475.2017.01.012
基于GBVS改进的Object Bank场景分类方法
New Object Bank Method for Scene Classification Based on GBVS
陈梦婷 1陈思喜2
作者信息
- 1. 同济大学电子与信息工程学院计算机系,上海 201804
- 2. 福州大学物理与信息工程学院,福建 福州 350116
- 折叠
摘要
Abstract
Object bank (OB) representation is a novel image representation for high-level visual tasks,which encodes semantic and spatial information of the objects within an image.However,the poor precision of the object detectors in OB method influences the extraction effect of high-level image feature.In order to solve this problem,a new OB method improved by Graph-Based Visual Saliency (GBVS) is proposed.Firstly,GBVS saliency model is utilized to process the image and detect the saliency regions and extract better high-level feature.The experiments results show that the proposed method performs better in classification and increases the classification accuracy of 4％.关键词
object bank/GBVS/场景分类/视觉显著性/高层特征Key words
object bank/GBVS/scene classification/visual saliency/high-level feature分类
信息技术与安全科学引用本文复制引用
陈梦婷,陈思喜..基于GBVS改进的Object Bank场景分类方法[J].计算机与现代化,2017,(1):61-64,70,5.
