A new box-counting method for image fractal dimension estimation

Xue, Shengjun; Jiang, Xinwei; Duan, Jimiao

doi:10.1109/compcomm.2017.8322847

Cited by 1 publication

(2 citation statements)

References 16 publications

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“…Then after counting the boxes, which are needed to cover the entire pattern. The fractal dimension can be calculated using the box counting algorithm [16] given below. For analyzing the complexity of certain geometry, fractal dimension can be used as a parameter.…”

Section: B Box Counting Methodsmentioning

confidence: 99%

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An Effective Galaxy Classification Using Fractal Analysis and Neural Network

Radhamani¹,

Sharif²,

Elmedany

2022

2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)

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Astronomy is always in a quest of revealing the mysteries of our Universe. There is a vast amount of astronomical data collected and this information comes from stars, galaxies and other celestial objects. While exploring this type of astronomical data, we can identify some complex selfsimilar patterns. Such self-similar patterns are shown in our own galaxy and are called fractals. This research work has been developed for finding such self-similarity that can be measured from galaxy clusters and this feature can be learned through a suitable neural network. This research work gives an insight about calculating the fractal dimension of galaxy images using a box counting algorithm and training the images using LeNet -5. The box counting fractal dimension is a specified range of values for each particular class of galaxy. By using the fractal dimension as a primary feature of different classes of galaxy and with the help of LeNet-5 network model classifying the galaxy images into ten specified classes according to its morphological properties. The model produced an accuracy of 74% when implemented with the baseline algorithm. When implemented with LeNet-5 it produced an accuracy of 96%. The precision recall and f1-Score value of the LeNet-5 model was also calculated. The precision recall and f1-Score value for class 1, class 2, class 4 and class 6 were higher than those of the other classes.

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Section: B Box Counting Methodsmentioning

confidence: 99%

“…Images of galaxy 10 come from DESI Legacy imaging survey and labels are from Galaxy Zoo [15]. Galaxy 10 decals [16] consist of DEcals images and DEcals campaign a, b, c. From the original Galaxy 10 they avoided 17 images of Edge on with Boxy Bulge and formed Galaxy 10 DEcals. The figure 2.…”

Section: A Dataset Explanationmentioning

confidence: 99%