Predicting Number of Unsupervised Clusters by Supervised Function

Srivisal, Chouvanee; Lursinsap, Chidchanok

doi:10.1109/cso.2009.132

Cited by 1 publication

(1 citation statement)

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“…The trend of the results indicated that with increased number of clusters the accuracy gradually increases [21]. [18][19][20]. A higher level of precision can be maintained by knowing the exact number of clusters in the data under investigation.…”

Section: Proposedmentioning

confidence: 96%

Extracting True Number of Clusters for Segmenting Image through Adaptive Finite Gaussian Mixture Model

Ahmad

Naeem²,

Habib³

2019

VFAST trans. softw. eng.

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Knowing exact number of clusters in a digital image significantly facilitates in precisely clustering an image. This paper proposes a new technique for extracting exact number of clusters from grey scale images. It analyzes the contents of the input image and adaptively reserves one distinct cluster for one distinct grey value. The total count of the grey values found in an image determines the exact number of clusters. Based on the contents of image, this number of clusters keeps on changing from image to image. After obtaining this number, it is given as an input to Gaussian Mixture Model (GMM) which clusters the image.GMM works with finite number of clusters and forms mixture of various spectral densities contained in that image. The proposed method facilitates GMM to adapt itself according to the changing number of clusters. Therefore, the proposed model along with the inclusion of GMM, is named as Adaptive Finite Gaussian Mixture Model (AFGMM). The clustering performance of AFGMM is evaluated through Mean Squared Error (MSE) and Peak Signal to Noise Ratio (PSNR). Both of these performance measuring methods confirmed that exact number of clusters is essentially important for reliably analyzing an image.

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