Hierarchy-Dependent Cross-Platform Multi-View Feature Learning for Venue Category Prediction

CORC > 沈阳自动化研究所 > 中国科学院沈阳自动化研究所 > 机器人学研究室

	Hierarchy-Dependent Cross-Platform Multi-View Feature Learning for Venue Category Prediction
	Jiang SQ(蒋树强)3,4; Mei SH(梅舒欢)1,4; Min WQ(闵巍庆)2,4
刊名	IEEE TRANSACTIONS ON MULTIMEDIA
	2019
卷号	21 期号:6 页码:1609-1619
关键词	Feature extraction knowledge transfer supervised learning video signal processing Web 2.0
ISSN号	1520-9210
产权排序	2
英文摘要	In this paper, we focus on visual venue category prediction, which can facilitate various applications for location-based service and personalization. Considering the complementarity of different media platforms, it is reasonable to leverage venue-relevant media data from different platforms to boost the prediction performance. Intuitively, recognizing one venue category involves multiple semantic cues, especially objects and scenes and, thus, they should contribute together to venue category prediction. In addition, these venues can be organized in a natural hierarchical structure, which provides prior knowledge to guide venue category estimation. Taking these aspects into account, we propose a Hierarchy-dependent Cross-platform Multi-view Feature Learning (HCM-FL) framework for venue category prediction from videos by leveraging images from other platforms. HCM-FL includes two major components, namely Cross-Platform Transfer Deep Learning (CPTDL) and Multi-View Feature Learning with the Hierarchical Venue Structure (MVFL-HVS). CPTDL is capable of reinforcing the learned deep network from videos using images from other platforms. Specifically, CPTDL first trained a deep network using videos. These images from other platforms are filtered by the learnt network and these selected images are then fed into this learnt network to enhance it. Two kinds of pre-trained networks on the ImageNet and Places dataset are employed. Therefore, we can harness both object-oriented and scene-oriented deep features through these enhanced deep networks. MVFL-HVS is then developed to enable multi-view feature fusion. It is capable of embedding the hierarchical structure ontology to support more discriminative joint feature learning. We conduct the experiment on videos from Vine and images from Foursquare. These experimental results demonstrate the advantage of our proposed framework in jointly utilizing multi-platform data, multi-view deep features, and hierarchical venue structure knowledge.
资助项目	Beijing Natural Science Foundation[4174106] ; National Natural Science Foundation of China[61532018] ; National Natural Science Foundation of China[61602437] ; Lenovo Outstanding Young Scientists Program ; National Program for Special Support of Eminent Professionals ; National Program for Support of Top-notch Young Professionals ; China Postdoctoral Science Foundation[2017T100110] ; State Key Laboratory of Robotics
WOS研究方向	Computer Science ; Telecommunications
语种	英语
WOS记录号	WOS:000469337400021
资助机构	Beijing Natural Science Foundation ; National Natural Science Foundation of China ; Lenovo Outstanding Young Scientists Program ; National Program for Special Support of Eminent Professionals ; National Program for Support of Top-notch Young Professionals ; China Postdoctoral Science Foundation ; State Key Laboratory of Robotics
内容类型	期刊论文
源URL	[http://ir.sia.cn/handle/173321/24724]
专题	沈阳自动化研究所_机器人学研究室
通讯作者	Jiang SQ(蒋树强)
作者单位	1.Shandong University of Science and Technology, Shandong 266590, China 2.State key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 3.University of Chinese Academy of Sciences,Beijing 100049, China 4.Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
推荐引用方式 GB/T 7714	Jiang SQ,Mei SH,Min WQ. Hierarchy-Dependent Cross-Platform Multi-View Feature Learning for Venue Category Prediction[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2019,21(6):1609-1619.
APA	Jiang SQ,Mei SH,&Min WQ.(2019).Hierarchy-Dependent Cross-Platform Multi-View Feature Learning for Venue Category Prediction.IEEE TRANSACTIONS ON MULTIMEDIA,21(6),1609-1619.
MLA	Jiang SQ,et al."Hierarchy-Dependent Cross-Platform Multi-View Feature Learning for Venue Category Prediction".IEEE TRANSACTIONS ON MULTIMEDIA 21.6(2019):1609-1619.