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.
|Title of host publication||Proceedings of the 9th International Conference on Computer Vision Theory and Application|
|Number of pages||8|
|Place of Publication||Lisbon, Portugal|
|ISBN (Print)||9897580042, 978-9897580048|
|Publication status||Published - 01.01.2014|
|Event||The International Conference on Computer Vision Theory and Applications - Lisbon / Lissabon, Portugal|
Duration: 05.01.2014 → 08.01.2014