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%。
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.
作者
赵小蕾
许喜斌
ZHAO Xiao-lei;XU Xi-bin(Xinhua College of Sun Yat-sen University, Guangzhou 510520, China;Guangdong Engineering Polytechnic, Guangzhou 510520, China)
出处
《计算机与现代化》
2019年第2期31-36,共6页
Computer and Modernization
基金
广州市科技计划项目(201804010265)
关键词
ROI
熵
互信息
K-MEANS
图像分类
region of interest
entropy
mutual information
K-means
image classification
