TY - JOUR
T1 - Accelerated intuitionistic fuzzy clustering for image segmentation
AU - Mújica-Vargas, Dante
AU - Rubio, José de Jesús
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
PY - 2021/11
Y1 - 2021/11
N2 - To improve processing time of the intuitionistic fuzzy C-means during color image segmentation, this paper proposes a scheme based on two clustering stages. In the first, a downsampled image is used to isolate the dominant color of the images by means of centroids calculating. Later, in the second stage these centroids are used during the image segmentation. With these two processes, an algorithmic acceleration of approximately eleven times can be guaranteed compared to the conventional algorithm. The effectiveness of this proposal is verified by experiments on the natural color images of datasets such as BSDS500 Alpert et al. Segmentation Evaluation Database, Sky dataset, Stony Bro- ok University Shadow and ISIC 2018. The quality of the segmentation was quantified using metrics and compared with other current methods of the state of the art. The results obtained show a superior performance of the proposed method both in segmentation and in processing time.
AB - To improve processing time of the intuitionistic fuzzy C-means during color image segmentation, this paper proposes a scheme based on two clustering stages. In the first, a downsampled image is used to isolate the dominant color of the images by means of centroids calculating. Later, in the second stage these centroids are used during the image segmentation. With these two processes, an algorithmic acceleration of approximately eleven times can be guaranteed compared to the conventional algorithm. The effectiveness of this proposal is verified by experiments on the natural color images of datasets such as BSDS500 Alpert et al. Segmentation Evaluation Database, Sky dataset, Stony Bro- ok University Shadow and ISIC 2018. The quality of the segmentation was quantified using metrics and compared with other current methods of the state of the art. The results obtained show a superior performance of the proposed method both in segmentation and in processing time.
KW - Algorithmic acceleration
KW - Color image segmentation
KW - Downsampled image
KW - Intuitionistic fuzzy C-means clustering
UR - http://www.scopus.com/inward/record.url?scp=85106501312&partnerID=8YFLogxK
U2 - 10.1007/s11760-021-01934-1
DO - 10.1007/s11760-021-01934-1
M3 - Artículo
AN - SCOPUS:85106501312
SN - 1863-1703
VL - 15
SP - 1845
EP - 1852
JO - Signal, Image and Video Processing
JF - Signal, Image and Video Processing
IS - 8
ER -