基于语义分割和上下文信息的图像场景解释研究进展

Ajay Koul, Apeksha Koul
{"title":"基于语义分割和上下文信息的图像场景解释研究进展","authors":"Ajay Koul, Apeksha Koul","doi":"10.1109/ICICT50521.2020.00031","DOIUrl":null,"url":null,"abstract":"The images in the scene consist of several objects that depict different relationships among themselves. Interpretation understands those relationships. Thus, scene interpretation is a scene description in which the scene models are consistent with the evidence, context information, and world knowledge. On one side, images in scene interpretation are useful in extracting the information that is related to the physical world and is meant for human operators. On the other side, it has always constituted a great challenge because of the varieties of complex objects due to which computer vision is not much capable to comprehend the information regarding the images in the scene. It also requires many efforts to extract the deeper meaning of the scene. So in review paper, we are going to summarize the methodology proposed by various researchers in terms of semantic segmentation and contextual information to interpret the images and highlight their contributions and challenges which still persist. Our analysis of the different methods proposed is also provided to draw some conclusions.","PeriodicalId":445000,"journal":{"name":"2020 3rd International Conference on Information and Computer Technologies (ICICT)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Semantic Segmentation and Contextual Information Based Image Scene Interpretation: A Review\",\"authors\":\"Ajay Koul, Apeksha Koul\",\"doi\":\"10.1109/ICICT50521.2020.00031\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The images in the scene consist of several objects that depict different relationships among themselves. Interpretation understands those relationships. Thus, scene interpretation is a scene description in which the scene models are consistent with the evidence, context information, and world knowledge. On one side, images in scene interpretation are useful in extracting the information that is related to the physical world and is meant for human operators. On the other side, it has always constituted a great challenge because of the varieties of complex objects due to which computer vision is not much capable to comprehend the information regarding the images in the scene. It also requires many efforts to extract the deeper meaning of the scene. So in review paper, we are going to summarize the methodology proposed by various researchers in terms of semantic segmentation and contextual information to interpret the images and highlight their contributions and challenges which still persist. Our analysis of the different methods proposed is also provided to draw some conclusions.\",\"PeriodicalId\":445000,\"journal\":{\"name\":\"2020 3rd International Conference on Information and Computer Technologies (ICICT)\",\"volume\":\"30 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 3rd International Conference on Information and Computer Technologies (ICICT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICICT50521.2020.00031\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 3rd International Conference on Information and Computer Technologies (ICICT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICICT50521.2020.00031","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

场景中的图像由几个物体组成,这些物体描绘了它们之间不同的关系。解释理解这些关系。因此,场景解释是一种场景描述,其中场景模型与证据、语境信息和世界知识相一致。一方面,场景解释中的图像有助于提取与物理世界相关的信息,这些信息是为人类操作员准备的。另一方面,由于复杂物体的多样性,计算机视觉无法理解场景中图像的信息,这一直是一个巨大的挑战。它也需要很多努力来提取场景的深层含义。因此,在本文中,我们将从语义分割和上下文信息两方面总结各种研究者提出的图像解释方法,并强调他们的贡献和仍然存在的挑战。本文还对提出的不同方法进行了分析,得出了一些结论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Semantic Segmentation and Contextual Information Based Image Scene Interpretation: A Review
The images in the scene consist of several objects that depict different relationships among themselves. Interpretation understands those relationships. Thus, scene interpretation is a scene description in which the scene models are consistent with the evidence, context information, and world knowledge. On one side, images in scene interpretation are useful in extracting the information that is related to the physical world and is meant for human operators. On the other side, it has always constituted a great challenge because of the varieties of complex objects due to which computer vision is not much capable to comprehend the information regarding the images in the scene. It also requires many efforts to extract the deeper meaning of the scene. So in review paper, we are going to summarize the methodology proposed by various researchers in terms of semantic segmentation and contextual information to interpret the images and highlight their contributions and challenges which still persist. Our analysis of the different methods proposed is also provided to draw some conclusions.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信