Capítulos de libros
Computing the number of groups for color image segmentation using competitive neural networks and fuzzy c-means
Registro en:
978-3-319-42293-0
0302-9743
Autor
Cervantes Canales, Jair; 101829
García Lamont, Farid; 216477
LOPEZ CHAU, ASDRUBAL; 100664
RUIZ CASTILLA, JOSE SERGIO; 231221
Cervantes Canales, Jair
García Lamont, Farid
LOPEZ CHAU, ASDRUBAL
RUIZ CASTILLA, JOSE SERGIO
Institución
Resumen
Se calcula la cantidad de grupos en que los vectores de color son agrupados usando fuzzy c-means Fuzzy C-means (FCM) is one of the most often techniques employed for color image segmentation; the drawback with this technique is the number of clusters the data, pixels’ colors, is grouped must be defined a priori. In this paper we present an approach to compute the number of clusters automatically. A competitive neural network (CNN) and a self-organizing map (SOM) are trained with chromaticity samples of different colors; the neural networks process each pixel of the image to segment, where the activation occurrences of each neuron are collected in a histogram. The number of clusters is set by computing the number of the most activated neurons. The number of clusters is adjusted by comparing the similitude of colors. We show successful segmentation results obtained using images of the Berkeley segmentation database by training only one time the CNN and SOM, using only chromaticity data.