计算机与现代化 ›› 2017, Vol. 0 ›› Issue (1): 61-64+70.doi: 10.3969/j.issn.1006-2475.2017.01.012

• 图像处理 • 上一篇    下一篇

基于GBVS改进的Object Bank场景分类方法

  

  1. (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)
  • 收稿日期:2016-06-16 出版日期:2017-01-12 发布日期:2017-01-11
  • 作者简介:陈梦婷(1992-),女,浙江温州人,同济大学电子与信息工程学院计算机系硕士研究生,研究方向:图像处理,场景分类; 陈思喜(1975-),男,福建福鼎人,福州大学物理与信息工程学院讲师,硕士,研究方向:数字媒体,虚拟互动。

New Object Bank Method for Scene Classification Based on GBVS

  1. (1. Department of Computer Science, College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China; 
    2. College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China)
  • Received:2016-06-16 Online:2017-01-12 Published:2017-01-11

摘要: Object Bank (OB)是一种基于物体的高层语义图像特征表示,该方法所提取的高层特征具有较丰富的场景语义。然而,该方法所采用的物体检测器检测物体的准确率并不高,从而影响高层特征的提取效果。针对OB方法中物体检测器准确率较低的缺点,提出一种基于Graph-Based Visual Saliency (GBVS)显著性分析算法改进的OB方法。先通过GBVS方法对图像进行显著性处理,计算图像中的显著性区域,然后结合OB方法中的物体检测器对显著区域进行检测,提取更具有场景语义的高层特征。实验结果表明,该方法突出了具有显著性的物体,提高了OB方法中目标检测器的准确率,在OB方法的基础上提取出了更具有显著性的图像特征OB方法提高了4%。,并在分类准确率上比

关键词: object bank, GBVS, 场景分类, 视觉显著性, 高层特征

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%.

Key words: object bank, GBVS, scene classification, visual saliency, high-level feature

中图分类号: