Abstract
In this paper we propose a new set of bio-inspired descriptors for image classification based on low-level processing performed by the retina. Taking as a starting point a descriptor called FREAK (Fast Retina Keypoint), we further extend it mimicking the center-surround organization of ganglion receptive fields. To test our approach we compared the performance of the original FREAK and our proposal on the 15 scene categories database. The results show that our approach outperforms the original FREAK for the scene classification task.
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Acknowledgement
We thank M. San Biagio for his support in the image classification algorithm. This research received financial support from the 7th Framework Programme for Research of the European Commission, under Grant agreement num 600847: RENVISION project of the Future and Emerging Technologies (FET) programme Neuro-bio-inspired systems (NBIS) FET-Proactive Initiative.
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Hilario Gomez, C., Medathati, K., Kornprobst, P., Murino, V., Sona, D. (2015). Improving FREAK Descriptor for Image Classification. In: Nalpantidis, L., Krüger, V., Eklundh, JO., Gasteratos, A. (eds) Computer Vision Systems. ICVS 2015. Lecture Notes in Computer Science(), vol 9163. Springer, Cham. https://doi.org/10.1007/978-3-319-20904-3_2
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DOI: https://doi.org/10.1007/978-3-319-20904-3_2
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