Anais do IX SIBGRAPI'96 (1996), 47-54
Neural-based Color Image Segmentation and Classification using Self-organizing Maps
Jander Moreira e Luciano da Fontoura Costa
Universidade Federal de São Carlos, IFSC - Instituto de Física de São Carlos
jander@dc.ufscar.br
- Abstract:
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This paper presents a method for color image segmentation
which uses classification to group pixels into regions. The
chromaticity is used as data source for the method because
it is normalized and considers only hue and saturation,
excluding the luminance component. The classification is
carried out by means of a self-organizing map (SOM), which
is employed to obtain the main chromaticities present in
the image. Then, each pixel is classified according to the
identified classes. The number of classes is a priori
unknown and the artificial neural network that implements
the SOM is used to determine the main classes. The
detection of the classes in the SOM is done by using a K-
means segmentation. The obtained results substantiate the
feasibility of the method, whose performance is compared,
for evaluation, to human-assisted segmentation. A
comparison of the method with a segmentation based on the
k-nearest-neighbor classification is also presented.
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