A Novel Image Classification Algorithm Based on Graph Theory

摘要：

　　In this paper the bag-of-words model is applied to image classification and improves the existing problems of the traditional bag-of-words method.We propose a method of combination of corner detection and graph theory for ROI region extraction and fuzzy membership degree.First using corner detection for images,then the ROI region is defined by the method of graph theory.Then the SIFT features of the ROI region are extracted and the visual dictionary is generated.The visual dictionary can be more accurate to describe the image features,which can reduce the influence of background information and other interference information.Secondly,the concept of fuzzy membership function and information of feature space is introduced to improve the image of the visual histogram.Finally,support vector machine classifier is used to classify.Through the experiment of the Caltech 100 database,the result shows that the method improves the accuracy of classification compared with the traditional method.

关键词： Bag-of-words Corner extraction Graph theory Fuzzy membership degree

作者: Shu-jian SHI

作者单位: Department of Computer Science,Shanghai Normal University Tianhua College,Shanghai,China

会议类型: 国际会议

会议名称: 2019 International Conference on Informatics, Control and Robotics 2019信息学、控制和机器人学国际会议(ICICR2019)

会议地点: 上海

会议语种:英文

页码: 206-212

在线出版日期: 2019-06-16（万方平台首次上网日期，不代表论文的发表时间）