Title :
Statistical Modeling for Image Matching in Large Image Databases
Author :
Källberg, David ; Seleznjev, Oleg ; Leonenko, Nikolaj ; Li, Haibo
Author_Institution :
Dept. Math. & Math. Stat., Umea Univ., Umea, Sweden
Abstract :
Matching a query (reference) image to an image extracted from a database containing (possibly) transformed image copies is an important retrieval task. In this paper we present a general method based on matching densities of the corresponding image feature vectors by using the Bregman distances. We consider statistical estimators for some quadratic entropy-type characteristics. In particular, the quadratic Bregman distances can be evaluated in image matching problems whenever images are modeled by random feature vectors in large image databases. Moreover, this method can be used for average case analysis for optimization of joining large databases.
Keywords :
entropy; image matching; statistical analysis; visual databases; image feature vector matching densities; large database optimization; large image databases; quadratic Bregman distances; quadratic entropy-type characteristics; query image matching; statistical estimators; statistical modeling; Approximation methods; Entropy; Histograms; Image color analysis; Image databases; Image matching; Bregman distance; approximate matching; asymptotic normality; database join; image matching; image retrieval;
Conference_Titel :
Internet of Things (iThings/CPSCom), 2011 International Conference on and 4th International Conference on Cyber, Physical and Social Computing
Conference_Location :
Dalian
Print_ISBN :
978-1-4577-1976-9
DOI :
10.1109/iThings/CPSCom.2011.117
