期刊
INFORMATION SCIENCES
卷 647, 期 -, 页码 -出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2023.119539
关键词
Image entropy; Auto encoder; Preprocessing; One-class classification
This paper proposes a novel preprocessing technique called image entropy equalization to eliminate the differences in image entropy. It compares the original and equalized images in various machine learning tasks, and shows that image entropy equalization can improve the AUC score for one-class autoencoder, as well as achieve fair results for classification and regression tasks.
Image entropy is the metric used to represent a complexity of an image. This study considers the hypothesis that image entropy differences affect machine learning algorithms' performance. This paper proposes a novel preprocessing technique, image entropy equalization, to delete the image entropy differences. The goal is to transform all images into the same entropy. Such a process is implemented by editing all images into the same histogram. Image entropy equalization is evaluated by comparing the original and equalized images in various machine learning tasks. The main advantage of image entropy equalization is to improve the AUC score for one-class autoencoder (OCAE). This result gives a new hypothesis that using image entropy equalization could improve various studies using autoencoder (AE). In addition, the proposed method shows fair results for classification and regression tasks. On the other hand, the main challenges are that the equalization process depends on a reference histogram and is affected by diverse backgrounds.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据