Entropy-Based Selection of Cluster Representatives for Document Image Compression.
In: SIAM Journal on Imaging Sciences, Jg. 12 (2019-10-01), Heft 4, S. 1720-1738
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Zugriff:
In this work, we introduce an efficient method for lossy compression of digitalized documents. The method uses a dictionary which consists of class representatives defined using a minimum entropy criterion. The algorithm initially identifies the different symbols contained in a document image, and then the symbols are grouped in classes by means of a hierarchic clustering algorithm. For each class, a representative is selected using the principle of minimum entropy and suitable similarity distances. The technique creates a file in which every object belonging to a class is replaced by its class representative. Finally, the resulting file is compressed. The performance of the proposed algorithm is assessed using digitized files from a standard database for document compression along with different resolutions. Comparisons against other state-of-the-art algorithms are performed in this manuscript. The results establish quantitatively that the present methodology is a more efficient technique. [ABSTRACT FROM AUTHOR]
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Titel: |
Entropy-Based Selection of Cluster Representatives for Document Image Compression.
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Autor/in / Beteiligte Person: | Muñoz-Pérez, Luis F. ; Guerrero, José A. ; Macías-Díaz, Jorge E. |
Zeitschrift: | SIAM Journal on Imaging Sciences, Jg. 12 (2019-10-01), Heft 4, S. 1720-1738 |
Veröffentlichung: | 2019 |
Medientyp: | academicJournal |
ISSN: | 1936-4954 (print) |
DOI: | 10.1137/19M1243312 |
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