Efficient classification of scanned media using spatial statistics

Ünal, Gözde and Gaurav, Sharma and Reiner, Eschbach (2010) Efficient classification of scanned media using spatial statistics. International Journal of Pattern Recognition and Artificial Intelligence, 24 (6). pp. 917-946. ISSN 0218-0014

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Photography, lithography, xerography, and inkjet printing are the dominant technologies for color printing. Images produced on these "different media" are often scanned either for the purpose of copying or creating an electronic representation. For an improved color calibration during scanning, a media identification from the scanned image data is desirable. In this paper, we propose an efficient algorithm for automated classification of input media into four major classes corresponding to photographic, lithographic, xerographic and inkjet. Our technique exploits the strong correlation between the type of input media and the spatial statistics of corresponding images, which are observed in the scanned images. We adopt ideas from spatial statistics literature, and design two spatial statistical measures of dispersion and periodicity, which are computed over spatial point patterns generated from blocks of the scanned image, and whose distributions provide the features for making a decision. We utilize extensive training data and determined well separated decision regions to classify the input media. We validate and tested our classification technique results over an independent extensive data set. The results demonstrate that the proposed method is able to distinguish between the different media with high reliability.
Item Type: Article
Uncontrolled Keywords: Pattern recognition; spatial statistics; distance based methods; dispersion methods; classification of marking and printing processes; classification of inkjet; xerographic; lithographic media
Subjects: Q Science > QA Mathematics > QA075 Electronic computers. Computer science
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Electronics
Faculty of Engineering and Natural Sciences
Depositing User: Gözde Ünal
Date Deposited: 27 Oct 2010 13:54
Last Modified: 29 Jul 2019 09:40
URI: https://research.sabanciuniv.edu/id/eprint/14932

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