Local Feature Extraction—What Receptive Field Size Should Be Used?
2009 (English)In: Proceedings of International Conference on Image Processing, Computer Vision and Pattern Recognition, 2009Conference paper (Refereed)
Biologically inspired hierarchical networks for image processing are based on parallel feature extraction across the image using feature detectors that have a limited Receptive Field (RF). It is, however, unclear how large these receptive fields should be. To study this, we ran systematic tests of various receptive field sizes using the same hierarchical network. After 40 epochs of training, we tested the network both by using similar but novel images of the same tropical cyclone that was used for training, and by using dissimilar images, depicting different cyclones. The results indicate that correct RF size is important for generalization in hierarchical networks, and that RF size should be chosen so that all RFs at least partially cover meaningful parts of the input image.
Place, publisher, year, edition, pages
pattern recognition, artificial neural networks, hierarchical networks
IdentifiersURN: urn:nbn:se:liu:diva-55074OAI: oai:DiVA.org:liu-55074DiVA: diva2:315295
International Conference on Image Processing, Computer Vision and Pattern Recognition