title: Color-to-grayscale: does the method matter in image recognition?
abstract: In image recognition it is often assumed the method used to convert color images to grayscale has little impact on recognition performance. We compare thirteen different grayscale algorithms with four types of image descriptors and demonstrate that this assumption is wrong: not all color-to-grayscale algorithms work equally well, even when using descriptors that are robust to changes in illumination. These methods are tested using a modern descriptor-based image recognition framework, on face, object, and texture datasets, with relatively few training instances. We identify a simple method that generally works best for face and object recognition, and two that work well for recognizing textures.
authors.affiliation: Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, United States of America. ckanan@ucsd.edu
authors.forename: Christopher
authors.initials: C
authors.lastname: Kanan
authors.forename: Garrison W
authors.initials: GW
authors.lastname: Cottrell
mesh_terms: D000465:Algorithms; D000818:Animals; D003116:Color; D019992:Databases as Topic; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*