SS-IVM-3.2

SEMANTICALLY RELEVANT SCENE DETECTION USING DEEP LEARNING

Dipanita Chakraborty, Werapon Chiracharit, Kosin Chamnongthai, King Mongkut's University of Technology Thonburi, Thailand
Session:
High Performance Image and Video Processing and Applications
Track:
Image, Video, and Multimedia (IVM)
Session Time:
Thu, 16 Dec, 14:00 - 16:00 Japan Standard Time (UTC +9)
Thu, 16 Dec, 05:00 - 07:00 Coordinated Universal Time
Thu, 16 Dec, 00:00 - 02:00 Eastern Standard Time (UTC -4)
Wed, 15 Dec, 21:00 - 23:00 Pacific Standard Time (UTC -7)
Session Co-Chairs:
Kosin Chamnongthai, King Mongkut's Unifersity of Technology Thonburi and Shogo Muramatsu, Niigata University
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Session SS-IVM-3
TH2.LS-C.1: SPATIALLY VARYING WHITE BALANCING FOR MIXED AND NON-UNIFORM ILLUMINANTS
Teruaki Akazawa, Yuma Kinoshita, Hitoshi Kiya, Tokyo Metropolitan University, Japan
TH2.LS-C.2: SEMANTICALLY RELEVANT SCENE DETECTION USING DEEP LEARNING
Dipanita Chakraborty, Werapon Chiracharit, Kosin Chamnongthai, King Mongkut's University of Technology Thonburi, Thailand
TH2.LS-C.3: Digital Halftone Classification using Simplified CNN and Stochastic Statistics
Jing-Ming Guo, Sankarasrinivasan Seshathiri, National Taiwan University of Science and Technology, Taiwan
TH2.LS-C.4: IMPLEMENTATION OF AVS3 MULTICAST SYSTEM BASED ON EMBMS
Lingfeng Fang, Chunhao Li, Songlin Sun, Beijing University of Posts and Telecommunications, China