title: Robust histogram construction from color invariants for object recognition.
abstract: An effective object recognition scheme is to represent and match images on the basis of histograms derived from photometric color invariants. A drawback, however, is that certain color invariant values become very unstable in the presence of sensor noise. To suppress the effect of noise for unstable color invariant values, in this paper, histograms are computed by variable kernel density estimators. To apply variable kernel density estimation in a principled way, models are proposed for the propagation of sensor noise through color invariant variables. As a result, the associated uncertainty is obtained for each color invariant value. The associated uncertainty is used to derive the parameterization of the variable kernel for the purpose of robust histogram construction. It is empirically verified that the proposed density estimator compares favorably to traditional histogram schemes for the purpose of object recognition.
authors.affiliation: Intelligent Sensory Information Systems, Department of Computer Science, Faculty of Science, University of Amsterdam, Kruislaan 403, 1098 SJ Amsterdam, The Netherlands. gevers@science.uva.nl
authors.forename: Theo
authors.initials: T
authors.lastname: Gevers
authors.forename: Harro
authors.initials: H
authors.lastname: Stokman
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003116:Color; D003124:Colorimetry / Q000379:methods*; D003198:Computer Simulation; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013269:Stochastic Processes; D013382:Subtraction Technique