Learning Scene Attribute for Scene Recognition

doi:10.1109/TMM.2019.2944241

	Learning Scene Attribute for Scene Recognition
	Zeng, Haitao 1,2; Song, Xinhang 2; Chen, Gongwei 2; Jiang, Shuqiang 2,3
	2020-06-01
发表期刊	IEEE TRANSACTIONS ON MULTIMEDIA
ISSN	1520-9210
卷号	22 期号:6 页码:1519-1530
摘要	Scene recognition has been a challenging task in the field of computer vision and multimedia for a long time. The current scene recognition works often extract object features and scene features through CNN, and combine these two types of features to obtain complementary and discriminative scene representations. However, when the scene categories are visually similar, the object features might lack of discriminations. Therefore, it may be debatable to consider only object features. In contrast to the existing works, in this paper, we discuss the discrimination of scene attributes in local regions and utilize scene attributes as the complementary features of object and scene features. We extract these visual features from two individual CNN branches, one extracting the global features of the image while the other extracting the features of local regions. Through contextual modeling framework, we aggregate these features and generate more discriminative scene representations, which achieve better performance than the feature aggregation of object and scene. Moreover, we achieve the new state-of-the-art performance on both standard scene recognition benchmarks by aggregating more complementary visual features: MIT67 (88.06%) and SUN397 (74.12%).
关键词	Feature extraction Visualization Semantics Context modeling Image recognition Computer vision Aggregates Scene recognition scene attribute
DOI	10.1109/TMM.2019.2944241
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61532018] ; National Natural Science Foundation of China[61902378] ; Beijing Natural Science Foundation[L182054] ; Beijing Natural Science Foundation[Z190020] ; National Program for Special Support of Eminent Professionals ; National Program for Support of Top-notch Young Professionals ; National Postdoctoral Program for Innovative Talents[BX201700255] ; China Postdoctoral Science Foundation[2018M631583]
WOS研究方向	Computer Science ; Telecommunications
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications
WOS记录号	WOS:000538033100012
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：9[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/15253
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Jiang, Shuqiang
作者单位	1.China Univ Min & Technol, Beijing 100083, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Zeng, Haitao,Song, Xinhang,Chen, Gongwei,et al. Learning Scene Attribute for Scene Recognition[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2020,22(6):1519-1530.
APA	Zeng, Haitao,Song, Xinhang,Chen, Gongwei,&Jiang, Shuqiang.(2020).Learning Scene Attribute for Scene Recognition.IEEE TRANSACTIONS ON MULTIMEDIA,22(6),1519-1530.
MLA	Zeng, Haitao,et al."Learning Scene Attribute for Scene Recognition".IEEE TRANSACTIONS ON MULTIMEDIA 22.6(2020):1519-1530.