DocumentCode :
595487
Title :
Towards automated classification of fine-art painting style: A comparative study
Author :
Arora, R.S. ; Elgammal, Ahmed
Author_Institution :
Dept. of Comput. Sci., Rutgers Univ., Piscataway, NJ, USA
fYear :
2012
fDate :
11-15 Nov. 2012
Firstpage :
3541
Lastpage :
3544
Abstract :
This paper presents a comparative study of different classification methodologies for the task of fine-art genre classification. 2-level comparative study is performed for this classification problem. 1st level reviews the performance of discriminative vs. generative models while 2nd level touches the features aspect of the paintings and compares semantic-level features vs low-level and intermediate level features present in the painting.
Keywords :
art; computer vision; image classification; 1st level; 2-level comparative study; automated classification; computer vision; fine-art genre classification; fine-art painting style; intermediate level features; low-level features; semantic-level features; Abstracts; Accuracy; Barium; Computer vision; Image color analysis; Painting; Support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition (ICPR), 2012 21st International Conference on
Conference_Location :
Tsukuba
ISSN :
1051-4651
Print_ISBN :
978-1-4673-2216-4
Type :
conf
Filename :
6460929
Link To Document :
https://search.ricest.ac.ir/dl/search/defaultta.aspx?DTC=49&DC=595487
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