| |
 |
| Paper: |
Galaxy Classification without Feature Extraction |
| Volume: |
461, Astronomical Data Analysis Software and Systems XXI |
| Page: |
561 |
| Authors: |
Polsterer, K. L.; Gieseke, F.; Kramer, O. |
| Abstract: |
The automatic classification of galaxies according to the different Hubble
types is a widely studied problem in the field of astronomy. The complexity of
this task led to projects like Galaxy Zoo which try to obtain labeled data
based on visual inspection by humans. Many automatic classification frameworks
are based on artificial neural networks (ANN) in combination with a feature
extraction step in the pre-processing phase. These approaches rely on labeled catalogs for
training the models. The small size of the typically used training sets,
however, limits the generalization performance of the resulting models. In this
work, we present a straightforward application of support vector machines (SVM)
for this type of classification tasks. The conducted experiments indicate that using
a sufficient number of labeled objects provided by the EFIGI catalog leads to
high-quality models. In contrast to standard approaches no additional feature
extraction is required. |
|
|
|
|
|
