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Research at St Andrews

Texture features for object salience

Research output: Contribution to journalArticle

DOI

Author(s)

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

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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.
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Details

Original languageEnglish
Pages (from-to)43-51
JournalImage and Vision Computing
Volume67
Early online date22 Sep 2017
DOIs
StateE-pub ahead of print - 22 Sep 2017

    Research areas

  • Texture, Colour, Salience, Attention, Benchmark

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