Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A fast k-means implementation using coresets
Proceedings of the twenty-second annual symposium on Computational geometry
Automatic seeded region growing for color image segmentation
Image and Vision Computing
Survey of clustering algorithms
IEEE Transactions on Neural Networks
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In this paper, we introduce a simple automatic color enteromorpha image segmentation algorithm. First, the color images are converted from RGB into NTSC color space. Then, we scale the data of the saturation channel in NTSC color space to the range of 0-255 and obtain its histogram. Using this histogram, we can obtain two peaks in the enteromorpha and background class respectively. Thus, two positions in these two classes can be obtained. Thirdly, those two positions are used as the centroids in the k-means algorithm. By means of k-means algorithm, every enteromorpha image can be divided into two classes: enteromorpha and background class. In fact, it is only a pre-processing for enteromorpha detection. Experimental results show that our approach can segment the enteromorpha images very accurately.