Galaxy classification using fractal signature

Fractal geometry is becoming increasingly important in the study of image characteristics. For recognition of regions and objects in natural scenes, there is always a need for features that are invariant and they provide a good set of descriptive values for the region. There are many fractal features that can be generated from an image. In this paper, fractal signatures of nearby galaxies are studied with the aim of classifying them. The fractal signature over a range of scales proved to be an efficient feature set with good discriminating power. Classifiers were designed using nearest neighbour method and neural network technique. Using the nearest distance approach, classification rate was found to be 92%. By the neural network method it has been found to increase to 95%.

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