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Paper Abstract and Keywords
Presentation 2017-10-06 08:30
Neural network-based estimation of degree of feeling that natural objects appear in photographic images
Manami Sasaki, Koji Iwano (Tokyo City Univ.)
Abstract (in Japanese) (See Japanese page) 
(in English) Recently, it is expected that the systems for automatically adding the suitable music to a target image will be developed. Toward realizing the systems, we propose a method to estimate the degree of feeling that natural objects appear in the target (photographic) images. The method uses neural network-based frameworks, and the network is trained by approximately 900 images. Evaluations were conducted by a task which classifies the impression of input images as “natural” or “artificial”. The best classification rate of 76.5% is observed when using “shape information (Bag-of-Keypoints)”, “the number of object candidates”, and “the number and area of detected face regions” as input features.
Keyword (in Japanese) (See Japanese page) 
(in English) Natural objects / Image impression estimation / Photographic image / Neural network / / / /  
Reference Info. ITE Tech. Rep., vol. 41, no. 33, ME2017-109, pp. 75-80, Oct. 2017.
Paper # ME2017-109 
Date of Issue 2017-09-29 (ME, AIT) 
ISSN Print edition: ISSN 1342-6893    Online edition: ISSN 2424-1970
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Conference Information
Committee ME AIT IEICE-IE  
Conference Date 2017-10-05 - 2017-10-06 
Place (in Japanese) (See Japanese page) 
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Paper Information
Registration To ME 
Conference Code 2017-10-ME-AIT-IE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Neural network-based estimation of degree of feeling that natural objects appear in photographic images 
Sub Title (in English)  
Keyword(1) Natural objects  
Keyword(2) Image impression estimation  
Keyword(3) Photographic image  
Keyword(4) Neural network  
1st Author's Name Manami Sasaki  
1st Author's Affiliation Tokyo City University (Tokyo City Univ.)
2nd Author's Name Koji Iwano  
2nd Author's Affiliation Tokyo City University (Tokyo City Univ.)
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Speaker Author-1 
Date Time 2017-10-06 08:30:00 
Presentation Time 25 minutes 
Registration for ME 
Paper # ME2017-109, AIT2017-168 
Volume (vol) vol.41 
Number (no) no.33 
Page pp.75-80 
Date of Issue 2017-09-29 (ME, AIT) 

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