Key-point Detection with Multi-layer Center-surround Inhibition

Foti Coleca, Sabrina Zîrnovean, Thomas Käster, Thomas Martinetz, Erhardt Barth


We present a biologically inspired algorithm for key-point detection based on multi-layer and nonlinear center-surround inhibition. A Bag-of-Visual-Words framework is used to evaluate the performance of the detector on the Oxford III-T Pet Dataset for pet recognition. The results demonstrate an increased performance of our algorithm compared to the SIFT key-point detector. We further improve the recognition rate by separately training codebooks for the ON- and OFF-type key points. The results show that our key-point detection algorithms outperform the SIFT detector by having a lower recognition-error rate over a whole range of different key-point densities. Randomly selected keypoints are also outperformed.

Original languageEnglish
Title of host publicationProceedings of the 9th International Conference on Computer Vision Theory and Application
EditorsSebastiano Battiato
Number of pages8
VolumeVol. 1
Place of PublicationLisbon, Portugal
Publication date01.01.2014
ISBN (Print)9897580042, 978-9897580048
Publication statusPublished - 01.01.2014
EventThe International Conference on Computer Vision Theory and Applications - Lisbon / Lissabon, Portugal
Duration: 05.01.201408.01.2014


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