Title
Human mimic color perception for segmentation of color images using a three-layered self-organizing map previously trained to classify color chromaticity.
Abstract
Most of the works addressing segmentation of color images use clustering-based methods; the drawback with such methods is that they require a priori knowledge of the amount of clusters, so the number of clusters is set depending on the nature of the scene so as not to lose color features of the scene. Other works that employ different unsupervised learning-based methods use the colors of the given image, but the classifying method employed is retrained again when a new image is given. Humans have the nature capability to: (1) recognize colors by using their previous knowledge, that is, they do not need to learn to identify colors every time they observe a new image and, (2) within a scene, humans can recognize regions or objects by their chromaticity features. Hence, in this paper we propose to emulate the human color perception for color image segmentation. We train a three-layered self-organizing map with chromaticity samples so that the neural network is able to segment color images by their chromaticity features. When training is finished, we use the same neural network to process several images, without training it again and without specifying, to some extent, the number of colors the image have. The hue component of colors is extracted by mapping the input image from the RGB space to the HSV space. We test our proposal using the Berkeley segmentation database and compare quantitatively our results with related works; according to the results comparison, we claim that our approach is competitive.
Year
DOI
Venue
2018
10.1007/s00521-016-2714-9
Neural Computing and Applications
Keywords
Field
DocType
Self-organizing maps, Color classification, Image segmentation, Color spaces
Computer vision,Color space,Color histogram,Color balance,Image segmentation,Artificial intelligence,RGB color model,False color,Mathematics,Color quantization,Color image
Journal
Volume
Issue
ISSN
30
3
1433-3058
Citations 
PageRank 
References 
1
0.34
31
Authors
3
Name
Order
Citations
PageRank
Farid GarcíA Lamont1699.58
Jair Cervantes217618.08
Asdrúbal López Chau38711.62