A Comparative Study on Distance Metrics in Self- Supervised Unstructured Road Detection Domain

2013-09-20
Özütemiz, Kadri Buğra
Hacınecipoğlu, Akif
Koku, Ahmet Buğra
Konukseven, Erhan İlhan
In pattern recognition/machine learning domain, selecting appropriate distance metric for the problem to find the distance between feature vectors or the distance between a feature vector and decision boundary is important in order to have satisfying results from the algorithm designed. In this study, in order to find the most appropriate distance metric to use in classification of road/non-road regions in streaming images, 6 different distance metrics are implemented and their classification performances are compared. The 6 distance metrics that are compared in the road detection domain are Manhattan distance, Euclidian distance, Mahalanobis distance, Chebyshev distance, Hellinger distance and Chi-square distance metrics, respectively.
Citation Formats
K. B. Özütemiz, A. Hacınecipoğlu, A. B. Koku, and E. İ. Konukseven, “A Comparative Study on Distance Metrics in Self- Supervised Unstructured Road Detection Domain,” presented at the Mechatronics and Machine Vision in Practice, M2VIP, 2013, Ankara, Türkiye, 2013, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/74076.