A Novel Image Classification Algorithm Based on Graph Theory

A Novel Image Classification Algorithm Based on Graph Theory

论文摘要

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.

论文目录

  • Introduction
  • The Basic Framework of the Bag-of-Words Model
  • ROI Regional Positioning
  •   Corner Detection
  •   Graph Theory Methods
  • Application of Fuzzy Theory
  • Experimental Results and Analysis
  • Conclusions
  • 文章来源

    类型: 国际会议

    作者: Shu-jian SHI

    来源: 2019 International Conference on Informatics, Control and Robotics (ICICR 2019) 2019-06-16

    年度: 2019

    分类: 基础科学,信息科技

    专业: 数学,计算机软件及计算机应用

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

    分类号: O157.5;TP391.41

    DOI: 10.26914/c.cnkihy.2019.077863

    页码: 219-225

    总页数: 7

    文件大小: 980k

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