Clustering validity index (CVI) plays an important role in data partitioning and image
segmentation. In this paper, a new CVI is proposed to perform the color image …

Color image segmentation is a much talked about topic in image processing, where there is
plenty of scope for improvement. A cluster validation index based novel method for …

We introduce a multi-feature optimization clustering algorithm for color image segmentation.
The local binary pattern, the mean of the min-max difference, and the color components are …

CGFFCM (Cluster-weight and Group-local Feature-weight learning in Fuzzy C-Means) is a
clustering-based color image segmentation approach. It applies an automatic cluster …

In this paper, five clustering techniques (k-means, ISODATA, merging, splitting and mean
shift techniques) used for colour image segmentation are presented. Two heuristic …

Color image segmentation is a crucial step in many computer vision and pattern recognition
applications. This paper introduces an adaptive and unsupervised approach based on …

This paper proposes an adaptive unsupervised scheme that could find diverse applications
in pattern recognition as well as in computer vision, particularly in color image segmentation …

This paper proposes a new improved mountain clustering technique, which is compared
with some of the existing techniques such as K-Means, FCM, EM and Modified Mountain …

The fuzzy c-means (FCM) algorithm is a popular method for data clustering and image
segmentation. However, the main problem of this algorithm is that it is very sensitive to the …

In this paper, we introduce two enhanced Fuzzy C-Means (FCM) clustering algorithms with
spatial constraints for noisy color image segmentation. The Rank M-type L (RM-L) and L …