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Methods | Features of methods |
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POP [10] | (i) One of the simplest methods. |
(ii) 16 × 16 × 16 color histogram using 4 bits per channel uniform quantization. |
(iii) K most-frequent colors in the color histogram are used for quantization. |
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MC [10] | (i) 32 × 32 × 32 color histogram using 5 bits per channel uniform quantization. |
(ii) It makes cubes that include all of the histogram. |
(iii) It repeatedly splits the cubes that have the greatest number of colors until K cubes are obtained. |
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OCT [9] | (i) Tree structure with up to 8 nodes as children, which can represent all colors in an image within an 8-level tree. |
(ii) Color distribution is represented using octree, which then prunes the nodes until K nodes remain. |
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KM [11, 14, 15] | (i) It starts with K random clusters. |
(ii) All of the input data are assigned to the cluster that has the minimum distance within the data. |
(iii) The centroid of the cluster is calculated as the average of the assigned data. |
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ART2 [12] | (i) Unsupervised learning model. |
(ii) It creates new clusters depending on a vigilance test. |
(iii) The palette color is chosen from the centroids of the most-frequent clusters. |
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SOM [1–5] | (i) Unsupervised learning model. |
(ii) One-dimensional self-organizing map with K neurons. |
(iii) It designates the minimum distance node as the “winner” node and then updates the weights of the winner node and neighbor nodes. |
(iv) It repeats the process until the sum of the weight change is less than a certain threshold. |
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