Results 1 -
4 of
4
Automated Classification of Galaxy Images
- Lecture
"... Abstract. In this paper we present an experimental study of the per-formance of three machine learning algorithms applied to the difficult problem of galaxy classification. We use the Naive Bayes classifier, the rule-induction algorithm C4.5 and a recently introduced classifier named random forest ( ..."
Abstract
-
Cited by 2 (0 self)
- Add to MetaCart
(Show Context)
Abstract. In this paper we present an experimental study of the per-formance of three machine learning algorithms applied to the difficult problem of galaxy classification. We use the Naive Bayes classifier, the rule-induction algorithm C4.5 and a recently introduced classifier named random forest (RF). We first employ image processing to standardize the images, eliminating the effects of orientation and scale, then perform principal component analysis to reduce the dimensionality of the data, and finally, classify the galaxy images. Our experiments show that RF obtains the best results considering three, five and seven galaxy types. 1
AUTOMATED STAR/GALAXY DISCRIMINATION IN MULTISPECTRAL WIDE-FIELD IMAGES
"... Abstract: In this paper we present an automated method for classifying astronomical objects in multi-spectral widefield images. The classification method is divided into three main stages. The first one consists of locating and matching the astronomical objects in the multi-spectral images. In the s ..."
Abstract
-
Cited by 1 (1 self)
- Add to MetaCart
(Show Context)
Abstract: In this paper we present an automated method for classifying astronomical objects in multi-spectral widefield images. The classification method is divided into three main stages. The first one consists of locating and matching the astronomical objects in the multi-spectral images. In the second stage we create a compact representation of each object applying principal component analysis to the images. In the last stage we classify the astronomical objects using locally weighted linear regression and a novel oversampling algorithm to deal with the unbalance that is inherent to this class of problems. Our experimental results show that our method performs accurate classification using small training sets and in the presence of significant class unbalance.
Automated Classification of Astronomical Objects in Multispectral Wide-Field Images
"... In this paper we present an automated method for clas-sifying astronomical objects in multispectral wide-field images. The method is divided into three main tasks. The first one consists of locating and matching the ob-jects in the multispectral images. In the second task we create a new representat ..."
Abstract
- Add to MetaCart
(Show Context)
In this paper we present an automated method for clas-sifying astronomical objects in multispectral wide-field images. The method is divided into three main tasks. The first one consists of locating and matching the ob-jects in the multispectral images. In the second task we create a new representation for each astronomical ob-ject using its multispectral images, and also we find a set of features using principal component analysis. In the last task we classify the astronomical objects using neural networks, locally weighted linear regression and random forest. Preliminary results show that the method obtains over 93 % accuracy classifying stars and galax-ies.
