Texture features for object salience

Kasim Terzić, Sai Krishna, J. M. H. du Buf

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
9 Downloads (Pure)

Abstract

Although texture is important for many vision-related tasks, it is not used in most salience models. As a consequence, there are images where all existing salience algorithms fail. We introduce a novel set of texture features built on top of a fast model of complex cells in striate cortex, i.e., visual area V1. The texture at each position is characterised by the two-dimensional local power spectrum obtained from Gabor filters which are tuned to many scales and orientations. We then apply a parametric model and describe the local spectrum by the combination of two one-dimensional Gaussian approximations: the scale and orientation distributions. The scale distribution indicates whether the texture has a dominant frequency and what frequency it is. Likewise, the orientation distribution attests the degree of anisotropy. We evaluate the features in combination with the state-of-the-art VOCUS2 salience algorithm. We found that using our novel texture features in addition to colour improves AUC by 3.8% on the PASCAL-S dataset when compared to the colour-only baseline, and by 62% on a novel texture-based dataset.
Original languageEnglish
Pages (from-to)43-51
JournalImage and Vision Computing
Volume67
Early online date22 Sept 2017
DOIs
Publication statusPublished - Nov 2017

Keywords

  • Texture
  • Colour
  • Salience
  • Attention
  • Benchmark

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