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|Title:||Automatic computing of number of clusters for color image segmentation employing fuzzy c-means by extracting chromaticity features of colors||Authors:||Jair Cervantes Canales
Farid García Lamont
ASDRUBAL LOPEZ CHAU
ARTURO YEE RENDON
|Keywords:||Competitive neural networks;Color classification;Image segmentation;Color spaces;info:eu-repo/classification/cti/7||Publisher:||Pattern Analysis and Applications||Description:||Se propone un enfoque para calcular el numero de grupos en que una imagen de color debe segmentarse utilizando fuzzy c-means
In this paper we introduce a method for color image segmentation by computing automatically the number of clusters the data, pixels, are divided into using fuzzy c-means. In several works the number of clusters is defined by the user. In other ones the number of clusters is computed by obtaining the number of dominant colors, which is determined with unsupervised neural networks (NN) trained with the image’s colors; the number of dominant colors is defined by the number of the most activated neurons. The drawbacks with this approach are as follows: (1) The NN must be trained every time a new image is given and (2) despite employing different color spaces, the intensity data of colors are used, so the undesired effects of nonuniform illumination may affect computing the number of dominant colors. Our proposal consists in processing the images with an unsupervised NN trained previously with chromaticity samples of different colors; the number of the neurons with the highest activation occurrences defines the number of clusters the image is segmented. By training the NN with chromatic data of colors it can be employed to process any image without training it again, and our approach is, to some extent, robust to non-uniform illumination. We perform experiments with the images of the Berkeley segmentation database, using competitive NN and self-organizing maps; we compute and compare the quantitative evaluation of the segmented images obtained with related works using the probabilistic random index and variation of information metrics.
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