Application of fractional bio-inspired filter for salient color detection

Juan Anaya-Jaimes; Angie García-Castro; J. A. Tenreiro-Machado; R. E. Gutiérrez-Carvajal

doi:10.1117/12.2587066

4 January 2021 Application of fractional bio-inspired filter for salient color detection

Juan Anaya-Jaimes, Angie García-Castro, J. A. Tenreiro-Machado, R. E. Gutiérrez-Carvajal

Proceedings Volume 11605, Thirteenth International Conference on Machine Vision; 116051X (2021) https://doi.org/10.1117/12.2587066
Event: Thirteenth International Conference on Machine Vision, 2020, Rome, Italy

Abstract

Computational modeling of visual attention has been a research field focused on emulating the behavior of biological visual systems in a given scenario, by using mechanisms developed for fixation prediction or salient region detection. In the literature, different approaches have been presented to emulate the interactions that occur in the early vision system of biological structures. However, mathematical modeling of these systems applying theories related to fractional operators could outperform the existing models. In this paper, we present a fractional bio-inspired filter for salient color detection in natural scenarios, based on the behavior and distribution of the cone photoreceptors cells in the retina. The filter was compared with two classic saliency algorithms over a natural color image dataset in terms of saliency prediction and processing time, using a Similarity (SIM) score and runtime performance, respectively. Our approach reach the second best result in therms of saliency prediction with a 48,9% of SIM with ground truth fixations maps and the fastest time response, with an average time of 0.12 s when processing a high resolution image, being 25% faster than Itti et al. algorithm, one of the most applied in robotic vision tasks.

Citation Download Citation

Juan Anaya-Jaimes, Angie García-Castro, J. A. Tenreiro-Machado, and R. E. Gutiérrez-Carvajal "Application of fractional bio-inspired filter for salient color detection", Proc. SPIE 11605, Thirteenth International Conference on Machine Vision, 116051X (4 January 2021); https://doi.org/10.1117/12.2587066

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