Color image segmentation using local histogram and self-organization of Kohonen feature map

Lo, You-Shen; Pei, Soo‐Chang

doi:10.1109/icip.1999.817107

Cited by 4 publications

(1 citation statement)

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“…Self organization of Kohonen Feature Map (SOFM) network is a powerful tool for clustering [17,27]. Ji and Park [26] proposed an algorithm for watershed segmentation based on SOM.…”

Section: Neural Network Based Techniquesmentioning

confidence: 99%

An Adaptive Color Image Segmentation

Deshmukh¹,

Shinde²

2006

ELCVIA

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A novel Adaptive Color Image Segmentation (ACIS) System for color image segmentation is presented. The proposed ACIS system uses a neural network with architecture similar to the multilayer perceptron (MLP) network. The main difference is that neurons here uses a multisigmoid activation function. The multisigmoid function is the key for segmentation. The number of steps i.e. thresholds in the multisigmoid function are dependant on the number of clusters in the image. The threshold values for detecting the clusters and their labels are found automatically from the first order derivative of histograms of saturation and intensity in the HSV color space. Here, the main use of neural network is to detect the number of objects automatically from an image. The advantage of this method is that no a priori knowledge is required to segment the color image. ACIS label the objects with their mean colors. The algorithm is found to be reliable and works satisfactorily on different kinds of color images. Experimental results show that the performance of ACIS is robust on noisy images also.

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“…Self organization of Kohonen Feature Map (SOFM) network is a powerful tool for clustering [17,27]. Ji and Park [26] proposed an algorithm for watershed segmentation based on SOM.…”

Section: Neural Network Based Techniquesmentioning

confidence: 99%