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Multimodal Data Management in Disks and Main Memory for Deep Learning

Title
Multimodal Data Management in Disks and Main Memory for Deep Learning
Translated Title
디스크 및 메인 메모리에서 딥러닝을 위한 멀티모달 데이터 관리
Authors
Sanghyeon Lee
DGIST Authors
Lee, Sanghyeon; Lee, Dooseok; Kim, Min-Soo
Advisor(s)
김민수
Co-Advisor(s)
Dooseok Lee
Issue Date
2019
Available Date
2019-10-03
Degree Date
2019-02
Type
Thesis
Table Of Contents
Ⅰ. INTRODUCTION·························································································· 1 ⅠⅠ. BACKGROUND························································································ 4 2.1 LMDB ···································································································· 4 2.2 Apache Kafka························································································· 5 ⅠⅠⅠ. MULTIMODAL DATA MANAGEMENT SYSTEM···································· 6 3.1 Batch and stream data ··········································································· 6 3.2 Batch and stream processing ································································· 7 ⅠV. MULTIMODAL DATA MANAGEMENT METHOD······································· 9 4.1 MDF ········································································································ 9 4.2 RID Table ······························································································ 11 4.3 Image MDF ·························································································· 13 4.4 Relational MDF ····················································································· 14 V. STREAM PROCESSING FOR DEEP LEARNING··············································· 16 5.1 Stream platform ····················································································· 17 5.2 Stream processing method ····································································· 18 5.3 Stream processing module ····································································· 20 VⅠ. EXPERIMENTS·························································································· 22 6.1 Experimental setup················································································· 22 6.2 Data conversion······················································································ 22 6.3 Stream processing for deep learning······················································· 24 6.3.1 Surveillance and drama video data··················································· 25 6.3.2 Surveillance data with noise images················································· 26 6.3.3 Unrelated video consisting of MasterCOCO dataset······························· 27 VⅠⅠ. CONCLUSION AND FUTURE WORK························································ 28 VⅠⅠⅠ. REFERENCE························································································· 29
URI
http://dgist.dcollection.net/common/orgView/200000171479
http://hdl.handle.net/20.500.11750/10739
DOI
10.22699/thesis.200000171479
Degree
MASTER
Department
Information and Communication Engineering
University
DGIST
Related Researcher
  • Author Lee, Doo Seok  
  • Research Interests CAGD(Computer Aided Geometric Design); Optimization; Smart Education; Machine Learning
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Collection:
Department of Information and Communication EngineeringThesesMaster


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