Título
Computing the number of groups for color image segmentation using competitive neural networks and fuzzy c-means
Autor
Jair Cervantes Canales
Farid García Lamont
ASDRUBAL LOPEZ CHAU
JOSE SERGIO RUIZ CASTILLA
Nivel de Acceso
Acceso Abierto
Materias
Color characterization - ([0302-9743, 978-3-319-42293-0]) - ([0302-9743, 978-3-319-42293-0]) Color spaces - ([0302-9743, 978-3-319-42293-0]) - ([0302-9743, 978-3-319-42293-0]) Competitive neural networks - ([0302-9743, 978-3-319-42293-0]) - ([0302-9743, 978-3-319-42293-0]) INGENIERÍA Y TECNOLOGÍA - (CTI)
Resumen o descripción
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.
Editor
Springer
Fecha de publicación
2016
Tipo de publicación
Capítulo de libro
Recurso de información
Fuente
0302-9743
978-3-319-42293-0
Idioma
Inglés
Audiencia
Estudiantes
Investigadores
Repositorio Orígen
REPOSITORIO INSTITUCIONAL DE LA UAEM
Descargas
284