TY - GEN
T1 - A color differentiated fuzzy c-means (CDFCM) based image segmentation algorithm
AU - Tsai, Min-Jen
AU - Chang, Hsuan Shao
PY - 2012
Y1 - 2012
N2 - Image segmentation is a very important process in digital image/video processing and computer vision applications. It is often used to partition an image into separated parts for further processes. For some applications (i.e., concept-based image retrieval), a successful segmentation algorithm is necessary to identity the objects effectively. In addition, how to tag the objects after the segmentation associated with keywords is also a challenge for researchers. In this study, we proposed a color differentiated fuzzy c-means (CDFCM) framework for effective image segmentation to achieve segmented objects within image which is useful for further annotation. In our experiments, we compared our approach with other FCM techniques on synthetic image with excellent performance. Furthermore, CDFCM outperforms other approaches by using the Berkeley image segmentation data set with layered annotation, which can be applied for additional operations.
AB - Image segmentation is a very important process in digital image/video processing and computer vision applications. It is often used to partition an image into separated parts for further processes. For some applications (i.e., concept-based image retrieval), a successful segmentation algorithm is necessary to identity the objects effectively. In addition, how to tag the objects after the segmentation associated with keywords is also a challenge for researchers. In this study, we proposed a color differentiated fuzzy c-means (CDFCM) framework for effective image segmentation to achieve segmented objects within image which is useful for further annotation. In our experiments, we compared our approach with other FCM techniques on synthetic image with excellent performance. Furthermore, CDFCM outperforms other approaches by using the Berkeley image segmentation data set with layered annotation, which can be applied for additional operations.
KW - color differentiated fuzzy c-means (CDFCM)
KW - image segmentation
UR - http://www.scopus.com/inward/record.url?scp=84874065321&partnerID=8YFLogxK
U2 - 10.1109/VCIP.2012.6410833
DO - 10.1109/VCIP.2012.6410833
M3 - Conference contribution
AN - SCOPUS:84874065321
SN - 9781467344050
T3 - 2012 IEEE Visual Communications and Image Processing, VCIP 2012
BT - 2012 IEEE Visual Communications and Image Processing, VCIP 2012
T2 - 2012 IEEE Visual Communications and Image Processing, VCIP 2012
Y2 - 27 November 2012 through 30 November 2012
ER -