计算机与现代化

• 图像处理 • 上一篇    下一篇

双重熵快速提取ROI图像优化分类方法

  

  1. (1.中山大学新华学院,广东广州510520;2.广东工程职业技术学院,广东广州510520)
  • 收稿日期:2018-06-30 出版日期:2019-02-25 发布日期:2019-02-26
  • 作者简介:赵小蕾(1988-),女,黑龙江齐齐哈尔人,讲师,硕士,研究方向:模式识别,多媒体信息处理,E-mail: xiaolei_zhao@foxmail.com; 许喜斌(1987-),男,广东揭阳人,高级工程师,硕士,研究方向:计算机视觉,智能嵌入式处理,E-mail: xxb8172006@126.com。
  • 基金资助:
    广州市科技计划项目(201804010265)

Optimized Image Classification Method by Double Entropy Fast Extraction ROI

  1. (1. Xinhua College of Sun Yat-sen University, Guangzhou  510520, China;
    2. Guangdong Engineering Polytechnic, Guangzhou  510520, China)
  • Received:2018-06-30 Online:2019-02-25 Published:2019-02-26

摘要: 提出一种基于颜色熵极值及颜色熵互信息的双重熵快速提取感兴趣区域(Region of Interest, ROI)的多特征图像优化分类方法。首先使用颜色熵极值性确定最相关区域,然后基于颜色熵互信息进行子区域增长,快速确定连续ROI区域,并基于所提取的ROI对图像进行Dense-SIFT特征描述,随后使用K-means聚类生成视觉词典,为了利用空间局部信息,采用金字塔匹配方法,最后将特征输入到SVM进行分类。分别在Caltech101和Caltech256数据库上选取8组数据进行实验,使用ROI提取算法获得的平均分类准确率较未使用之前提高6.86%,收敛速率提升近一半。加入颜色熵、颜色三阶矩特征后,平均分类准确率进一步提高2.36%,较改进之前总共提高9.22%。

关键词: ROI, 熵, 互信息, K-means, 图像分类

Abstract: A multi-features optimized image classification method based on region of interest (ROI) extracting method using color entropy extreme value and color entropy mutual information is proposed. Firstly, the most relevant region is determined by the color entropy extreme value, then the continuous ROI region is determined by using entropy mutual information to grow sub-region quickly. The Dense-SIFT characteristic description is extracted based on the ROI region, and a visual dictionary is generated by K-means method. In order to use the spatial local information, the pyramid matching method is adopted. Finally, the characteristics are input into SVM for classification. In the Caltech101 and Caltech256 databases, 8 data sets are selected for experiment. The average classification accuracy is improved by 6.86% obtained by using ROI extraction algorithm and the convergence rate is improved by nearly half. After adding the color entropy and the color third moments, the classification accuracy is further increased by 2.36%, it is 9.22% higher than before improvement totally.

Key words: region of interest, entropy, mutual information, K-means, image classification

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