Color reduction and estimation of the number of dominant colors by using a self-growing and self-organized neural gas
A new method for color reduction in a digital image is proposed, which is based on the development of a new neural network classifier and on a new method for Estimation of the Most Important Classes (EMIC). The proposed neural network combines the features of the well-known Growing Neural Gas (GNG) and the Kohonen Self-Organized Feature Map (KSOFM) neural networks. We call the new neural network Self-Growing and Self-Organized Neural Gas (SGONG). This combination produces a new neural network with outstanding features. The proposed technique utilizes the GNG mechanism of growing the neural lattice and the KSOFM leaning adaptation mechanism. Besides, introducing a number of criteria that have an effect on inserting or removing neurons, it is able to automatically define the number of the created neurons and their topology. Moreover, applying the EMIC method, the produced classes can be filtered and the most important classes can be found. The combination of SGONG and EMIC results in retaining the isolated and significant colors with the minimum number of color classes. The above techniques are able to be fed by both color and spatial features. For this reason a similarity function is used for vector comparison. The method is applicable to any type of color images and it can accommodate any type of color space.
color reduction self-organized neural networks color quantization segmentation