Deep Patch Representations with Shared Codebook for Scene Classification

doi:10.1145/3231738

CORC > 计算技术研究所 > 中国科学院计算技术研究所 > 中国科学院计算技术研究所期刊论文 > 英文

	Deep Patch Representations with Shared Codebook for Scene Classification
	Song, Xinhang; Chen, Gongwei; Jiang, Shuqiang; Liu, Linhu
刊名	ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
	2019-02-01
卷号	15 期号:1 页码:17
关键词	Scene classification convolutional neural network feature encoding shared codebook
ISSN号	1551-6857
DOI	10.1145/3231738
英文摘要	Scene classification is a challenging problem. Compared with object images, scene images are more abstract, as they are composed of objects. Object and scene images have different characteristics with different scales and composition structures. How to effectively integrate the local mid-level semantic representations including both object and scene concepts needs to be investigated, which is an important aspect for scene classification. In this article, the idea of a sharing codebook is introduced by organically integrating deep learning, concept feature, and local feature encoding techniques. More specifically, the shared local feature codebook is generated from the combined ImageNet1K and Places365 concepts (Mixed1365) using convolutional neural networks. As the Mixed1365 features cover all the semantic information including both object and scene concepts, we can extract a shared codebook from the Mixed1365 features, which only contain a subset of the whole 1,365 concepts with the same codebook size. The shared codebook can not only provide complementary representations without additional codebook training but also be adaptively extracted toward different scene classification tasks. A method of fusing the encoded features with both the original codebook and the shared codebook is proposed for scene classification. In this way, more comprehensive and representative image features can be generated for classification. Extensive experimentations conducted on two public datasets validate the effectiveness of the proposed method. Besides, some useful observations are also revealed to show the advantage of shared codebook.
资助项目	National Natural Science Foundation of China[61532018] ; Lenovo Outstanding Young Scientists Program ; National Program for Special Support of Eminent Professionals ; National Program for Support of Top-notch Young Professionals ; National Postdoctoral Program for Innovative Talents[BX201700255]
WOS研究方向	Computer Science
语种	英语
出版者	ASSOC COMPUTING MACHINERY
WOS记录号	WOS:000459798100005
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/3424]
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	Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, 6 Kexueyuan South Rd, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Song, Xinhang,Chen, Gongwei,Jiang, Shuqiang,et al. Deep Patch Representations with Shared Codebook for Scene Classification[J]. ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,2019,15(1):17.
APA	Song, Xinhang,Chen, Gongwei,Jiang, Shuqiang,&Liu, Linhu.(2019).Deep Patch Representations with Shared Codebook for Scene Classification.ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,15(1),17.
MLA	Song, Xinhang,et al."Deep Patch Representations with Shared Codebook for Scene Classification".ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 15.1(2019):17.