TY - GEN
T1 - Accuracy enhancement of image segmentation using adaptive anisotropic diffusion
AU - Lim, Jae Sung
AU - Cho, Sung In
AU - Kim, Young Hwan
PY - 2013
Y1 - 2013
N2 - This paper proposes a new pre-processing method to enhance accuracy of image segmentation. The proposed method produces a de-textured image which gives appropriate help to improve the segmentation quality when the existing segmentation method, histogram-based clustering, is applied on the simplified image. For obtaining this simplified image, we perform the de-texturing using an adaptive anisotropic diffusion model. Then, the histogram-based clustering is performed on the de-textured image to obtain segmentation results. In the experiments the Berkeley Segmentation Dataset, probabilistic rand index (PRI) and segmentation covering (SC) values are used for evaluating the segmentation quality. Experimental results showed that the segmentation accuracy of the histogram-based clustering was improved by using pre-processing in terms of average PRI and SC values by up to 0.86%, 14%, respectively.
AB - This paper proposes a new pre-processing method to enhance accuracy of image segmentation. The proposed method produces a de-textured image which gives appropriate help to improve the segmentation quality when the existing segmentation method, histogram-based clustering, is applied on the simplified image. For obtaining this simplified image, we perform the de-texturing using an adaptive anisotropic diffusion model. Then, the histogram-based clustering is performed on the de-textured image to obtain segmentation results. In the experiments the Berkeley Segmentation Dataset, probabilistic rand index (PRI) and segmentation covering (SC) values are used for evaluating the segmentation quality. Experimental results showed that the segmentation accuracy of the histogram-based clustering was improved by using pre-processing in terms of average PRI and SC values by up to 0.86%, 14%, respectively.
KW - anisotropic diffusion
KW - de-texture
KW - edge-preserving smooth
KW - histogram-based K-means clustering (HKMC)
UR - http://www.scopus.com/inward/record.url?scp=84892643405&partnerID=8YFLogxK
U2 - 10.1109/GCCE.2013.6664887
DO - 10.1109/GCCE.2013.6664887
M3 - Conference contribution
AN - SCOPUS:84892643405
SN - 9781479908929
T3 - 2013 IEEE 2nd Global Conference on Consumer Electronics, GCCE 2013
SP - 451
EP - 452
BT - 2013 IEEE 2nd Global Conference on Consumer Electronics, GCCE 2013
T2 - 2013 IEEE 2nd Global Conference on Consumer Electronics, GCCE 2013
Y2 - 1 October 2013 through 4 October 2013
ER -