A parametric spectral model for texture-based salience
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We present a novel saliency mechanism based on texture. Local texture at each pixel is characterised by the 2D spectrum obtained from oriented Gabor filters. We then apply a parametric model and describe the texture at each pixel by a combination of two 1D Gaussian approximations. This results in a simple model which consists of only four parameters. These four parameters are then used as feature channels and standard Difference-of-Gaussian blob detection is applied in order to detect salient areas in the image, similar to the Itti and Koch model. Finally, a diffusion process is used to sharpen the resulting regions. Evaluation on a large saliency dataset shows a significant improvement of our method over the baseline Itti and Koch model.
Terzić , K , Krishna , S & Du Buf , J M H 2015 , A parametric spectral model for texture-based salience . in J Gall , P Gehler & B Leibe (eds) , Pattern Recognition : 37th German Conference, GCPR 2015, Aachen, Germany, October 7-10, 2015, Proceedings . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , vol. 9358 , Springer , Cham , pp. 331-342 , 37th German Conference on Pattern Recognition, GCPR 2015 , Aachen , Germany , 7/10/15 . DOI: 10.1007/978-3-319-24947-6_27conference
© 2015, Springer International Publishing Switzerland. This work has been made available online in accordance with the publisher’s policies. This is the author created accepted version manuscript following peer review and as such may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1007/978-3-319-24947-6_27
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