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计算机系统应用:2018,27(9):193-198
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改进随机森林算法的图像分类应用
张志禹, 吉元元, 满蔚仕
(西安理工大学 自动化与信息工程学院, 西安 710048)
Image Classification Application Based on Improved Random Forest Algorithm
ZHANG Zhi-Yu, JI Yuan-Yuan, MAN Wei-Shi
(Faculty of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China)
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投稿时间:2018-01-23    修订日期:2018-02-27
中文摘要: 针对随机森林算法中节点分裂方式单一且相似的问题,提出一种改进节点分裂方式的优化算法,将算法中独立的节点分裂方式ID3与CART进行重新组合,通过自适应参数选择得到新的分裂规则,用于最优属性的选择划分并应用于图像分类问题.首先以词袋模型为基础,加入空间金字塔结构来提取图像特征,并将其量化成视觉词汇,最后结合Spark平台用改进节点分裂方式的随机森林算法实现图像分类.实验结果表明,通过选择组合算法的最优系数,该算法有效提高图像分类准确率,并保证算法运行效率.
Abstract:An improved random forest node splitting algorithm is proposed in this study for improving the accuracy of image classification. The independent splitting method ID3 and CART are re-combined, and new splitting rules are obtained by adaptive parameter selection. On the basis of the bag-of-words model, the spatial pyramid model is introduced to extract image features. After dividing the image into different grids, k-means algorithm is then used to character clustering. Finally, it uses the algorithm for verification on a large number of images on Spark. The results show that the algorithm can be applied to distributed systems, and can greatly improve the classification accuracy while ensuring the efficiency of the algorithm at the same time.
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基金项目:国家自然科学基金(41390454)
引用文本:
张志禹,吉元元,满蔚仕.改进随机森林算法的图像分类应用.计算机系统应用,2018,27(9):193-198
ZHANG Zhi-Yu,JI Yuan-Yuan,MAN Wei-Shi.Image Classification Application Based on Improved Random Forest Algorithm.COMPUTER SYSTEMS APPLICATIONS,2018,27(9):193-198

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