Statistical modeling for image matching in large image databases

• 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 quEDratic entropy-type characteristics. In particular, the quEDratic 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. © 2011 IEEE.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)

Nyckelord

Approximate matching

Asymptotic normality

Bregman distance

Database join

Image matching

Image retrieval

Publikations- och innehållstyp

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