Improving FREAK Descriptor for Image Classification

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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