{"title":"基于显著性信息和马赛克的数据增强方法用于密集遮挡物体识别","authors":"Ying Tong, Xiangfeng Luo, Liyan Ma, Shaorong Xie, Wenbin Yang, Yinsai Guo","doi":"10.1007/s10044-024-01258-z","DOIUrl":null,"url":null,"abstract":"<p>Data augmentation methods are crucial to improve the accuracy of densely occluded object recognition in the scene where the quantity and diversity of training images are insufficient. However, the current methods that use regional dropping and mixing strategies suffer from the problem of missing foreground objects and redundant background features, which can lead to densely occluded object recognition issues in classification or detection tasks. Herein, saliency information and mosaic based data augmentation method for densely occluded object recognition is proposed, which utilizes saliency information as prior knowledge to supervise the mosaic process of training images containing densely occluded objects. And the method uses fogging processing and class label mixing to construct new augmented images, in order to improve the accuracy of image classification and object recognition tasks by augmenting the quantity and diversity of training images. Extensive experiments on different classification datasets with various CNN architectures prove the effectiveness of our method.</p>","PeriodicalId":54639,"journal":{"name":"Pattern Analysis and Applications","volume":"43 1","pages":""},"PeriodicalIF":3.7000,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Saliency information and mosaic based data augmentation method for densely occluded object recognition\",\"authors\":\"Ying Tong, Xiangfeng Luo, Liyan Ma, Shaorong Xie, Wenbin Yang, Yinsai Guo\",\"doi\":\"10.1007/s10044-024-01258-z\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Data augmentation methods are crucial to improve the accuracy of densely occluded object recognition in the scene where the quantity and diversity of training images are insufficient. However, the current methods that use regional dropping and mixing strategies suffer from the problem of missing foreground objects and redundant background features, which can lead to densely occluded object recognition issues in classification or detection tasks. Herein, saliency information and mosaic based data augmentation method for densely occluded object recognition is proposed, which utilizes saliency information as prior knowledge to supervise the mosaic process of training images containing densely occluded objects. And the method uses fogging processing and class label mixing to construct new augmented images, in order to improve the accuracy of image classification and object recognition tasks by augmenting the quantity and diversity of training images. Extensive experiments on different classification datasets with various CNN architectures prove the effectiveness of our method.</p>\",\"PeriodicalId\":54639,\"journal\":{\"name\":\"Pattern Analysis and Applications\",\"volume\":\"43 1\",\"pages\":\"\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-03-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Pattern Analysis and Applications\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s10044-024-01258-z\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pattern Analysis and Applications","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s10044-024-01258-z","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Saliency information and mosaic based data augmentation method for densely occluded object recognition
Data augmentation methods are crucial to improve the accuracy of densely occluded object recognition in the scene where the quantity and diversity of training images are insufficient. However, the current methods that use regional dropping and mixing strategies suffer from the problem of missing foreground objects and redundant background features, which can lead to densely occluded object recognition issues in classification or detection tasks. Herein, saliency information and mosaic based data augmentation method for densely occluded object recognition is proposed, which utilizes saliency information as prior knowledge to supervise the mosaic process of training images containing densely occluded objects. And the method uses fogging processing and class label mixing to construct new augmented images, in order to improve the accuracy of image classification and object recognition tasks by augmenting the quantity and diversity of training images. Extensive experiments on different classification datasets with various CNN architectures prove the effectiveness of our method.
期刊介绍:
The journal publishes high quality articles in areas of fundamental research in intelligent pattern analysis and applications in computer science and engineering. It aims to provide a forum for original research which describes novel pattern analysis techniques and industrial applications of the current technology. In addition, the journal will also publish articles on pattern analysis applications in medical imaging. The journal solicits articles that detail new technology and methods for pattern recognition and analysis in applied domains including, but not limited to, computer vision and image processing, speech analysis, robotics, multimedia, document analysis, character recognition, knowledge engineering for pattern recognition, fractal analysis, and intelligent control. The journal publishes articles on the use of advanced pattern recognition and analysis methods including statistical techniques, neural networks, genetic algorithms, fuzzy pattern recognition, machine learning, and hardware implementations which are either relevant to the development of pattern analysis as a research area or detail novel pattern analysis applications. Papers proposing new classifier systems or their development, pattern analysis systems for real-time applications, fuzzy and temporal pattern recognition and uncertainty management in applied pattern recognition are particularly solicited.