Title :
Image Feature Extraction and Recognition of Abstractionism and Realism Style of Indonesian Paintings
Author :
Tieta Antaresti, R.P. ; Arymurthy, Aniati Murni
Author_Institution :
Fac. of Comput. Sci., Univ. of Indonesia, Depok, Indonesia
Abstract :
This paper chooses and evaluates three feature vectors and their augmented feature vector for recognizing the styles of Indonesian paintings. The three feature extraction methods include the Gabor wavelet, histogram analysis, and number-of-edge analysis. The recognition purpose is to discriminate between the abstractionism and the realism styles of Indonesian paintings. The experimental results using 115 painting images show that the use of number-of-edge features has given the best result with 66.23% accuracy.
Keywords :
art; augmented reality; edge detection; feature extraction; wavelet transforms; Gabor wavelet; Indonesian painting; abstractionism; augmented feature vector; histogram analysis; image feature extraction; image recognition; number-of-edge analysis; Feature extraction; Histograms; Image edge detection; Painting; Support vector machine classification; Testing; Canny edge detection; Gabor wavelet; Indonesian paintings; feature extraction; histogram; visual arts;
Conference_Titel :
Advances in Computing, Control and Telecommunication Technologies (ACT), 2010 Second International Conference on
Conference_Location :
Jakarta
Print_ISBN :
978-1-4244-8746-2
Electronic_ISBN :
978-0-7695-4269-0