Bird feather color feature extraction method
A color feature and extraction method technology, applied in digital image processing and color fields, can solve the problems of different color feature extraction and representative colors not being extracted, so as to expand the design field, increase added value, and broaden the market prospect Effect
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Embodiment 1
[0030] A kind of bird feather color feature extraction method of the present embodiment, comprises the steps:
[0031] a) pick a photo of a bird;
[0032] b) Use Matlab software to open one or two bird photos that require color feature extraction;
[0033] c) Use Matlab software to extract the RGB values of all pixels in the opened bird photo,
[0034] d) color-separate the bird photos according to the improved K-means clustering algorithm;
[0035] d1) The selection of the first initial cluster center is fixed as big red (R=255, G=0, B=0), and the second initial cluster center is green (R=0, G=255, B=0 ), the third initial point is blue (R=0, G=0, B=255);
[0036] d2) Calculate the next initial cluster center in turn by distance measurement method until all categories of initial cluster centers are selected, then calculate the distance between the remaining data and the first, second, and third initial cluster centers, and the maximum distance is the fourth cluster cent...
Embodiment 2
[0049] A kind of bird feather color feature extraction method of the present embodiment, comprises the steps:
[0050] a) Select a peacock feather photo to extract its color features;
[0051] b) Use Matlab software to open one or two bird photos that require color feature extraction;
[0052] c) using Matlab software to extract the RGB values of all pixels in the opened bird photos;
[0053] d) color-separate the bird photos according to the improved K-Means clustering algorithm;
[0054] d1) The selection of the first initial cluster center is fixed as big red (R=255, G=0, B=0), and the second initial cluster center is green (R=0, G=255, B=0 ), the third initial point is blue (R=0, G=0, B=255);
[0055] d2) Calculate the next initial cluster center in turn by distance measurement method until all categories of initial cluster centers are selected, then calculate the distance between the remaining data and the first, second, and third initial cluster centers, and the max...
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