This paper presents a new approach to image segmentation using Pillar K-means algorithm. This

segmentation method includes a new mechanism for grouping the elements of high resolution images in order to

improve accuracy and reduce the computation time. The system uses K-means for image segmentation optimized by

the algorithm after Pillar. The Pillar algorithm considers the placement of pillars should be located as far from each

other to resist the pressure distribution of a roof, as same as the number of centroids between the data distribution. This

algorithm is able to optimize the K-means clustering for image segmentation in the aspects of accuracy and

computation time. This algorithm distributes all initial centroids according to the maximum cumulative distance metric.

This paper evaluates the proposed approach for image segmentation by comparing with K-means clustering

algorithm and Gaussian mixture model and the participation of RGB, HSV, HSL and CIELAB color spaces.

-This paper presents a new approach to image segmentation using Pillar K-means algorithm. This

segmentation method includes a new mechanism for grouping the elements of high resolution images in order to

improve accuracy and reduce the computation time. The system uses K-means for image segmentation optimized by

the algorithm after Pillar. The Pillar algorithm considers the placement of pillars should be located as far from each

other to resist the pressure distribution of a roof, as same as the number of centroids between the data distribution. This

algorithm is able to optimize the K-means clustering for image segmentation in the aspects of accuracy and

computation time. This algorithm distributes all initial centroids according to the maximum cumulative distance metric.　

This paper evaluates the proposed approach for image segmentation by comparing with K-means clustering

algorithm and Gaussian mixture model and the participation of RGB, HSV, HSL and CIELAB color spaces.