Shape index descriptors applied to texture-based galaxy analysis

TY - GEN

T1 - Shape index descriptors applied to texture-based galaxy analysis

AU - Pedersen, Kim Steenstrup

AU - Stensbo-Smidt, Kristoffer

AU - Zirm, Andrew Wasmuth

AU - Igel, Christian

PY - 2013

Y1 - 2013

N2 - A texture descriptor based on the shape index and the accompanying curvedness measure is proposed, and it is evaluated for the automated analysis of astronomical image data. A representative sample of images of low-red shift galaxies from the Sloan Digital Sky Survey (SDSS) serves as a test bed. The goal of applying texture descriptors to these data is to extract novel information about galaxies, information which is often lost in more traditional analysis. In this study, we build a regression model for predicting a spectroscopic quantity, the specific star-formation rate (sSFR). As texture features we consider multi-scale gradient orientation histograms as well as multi-scale shape index histograms, which lead to a new descriptor. Our results show that we can successfully predict spectroscopic quantities from the texture in optical multi-band images. We successfully recover the observed bi-modal distribution of galaxies into quiescent and star-forming. The state-of-the-art for predicting the sSFR is a color-based physical model. We significantly improve its accuracy by augmenting the model with texture information. This study is the first step towards enabling the quantification of physical galaxy properties from imaging data alone.

AB - A texture descriptor based on the shape index and the accompanying curvedness measure is proposed, and it is evaluated for the automated analysis of astronomical image data. A representative sample of images of low-red shift galaxies from the Sloan Digital Sky Survey (SDSS) serves as a test bed. The goal of applying texture descriptors to these data is to extract novel information about galaxies, information which is often lost in more traditional analysis. In this study, we build a regression model for predicting a spectroscopic quantity, the specific star-formation rate (sSFR). As texture features we consider multi-scale gradient orientation histograms as well as multi-scale shape index histograms, which lead to a new descriptor. Our results show that we can successfully predict spectroscopic quantities from the texture in optical multi-band images. We successfully recover the observed bi-modal distribution of galaxies into quiescent and star-forming. The state-of-the-art for predicting the sSFR is a color-based physical model. We significantly improve its accuracy by augmenting the model with texture information. This study is the first step towards enabling the quantification of physical galaxy properties from imaging data alone.

KW - Faculty of Science

KW - Texture analysis

KW - Image analysis in astronomy

U2 - 10.1109/ICCV.2013.303

DO - 10.1109/ICCV.2013.303

M3 - Article in proceedings

SP - 2440

EP - 2447

BT - 2013 IEEE International Conference on Computer Vision (ICCV)

PB - IEEE

T2 - IEEE International Conference on Computer Vision

Y2 - 1 December 2013 through 8 December 2013

ER -