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General Information
    • ISSN: 2301-3559
    • Frequency: Quarterly
    • DOI: 10.18178/LNSE
    • Editor-in-Chief: Prof. Jemal Antidze
    • Executive Editor: Ms. Nina Lee
    • Abstracting/ Indexing: EI (INSPEC, IET), Electronic Journals Library,  Ulrich's Periodicals Directory, International Computer Science Digital Library (ICSDL), ProQuest and Google Scholar.
    • E-mail: lnse@ejournal.net
Prof. Jemal Antidze
I. Vekua Scientific Institute of Applied Mathematics
Tbilisi State University, Georgia
I'm happy to take on the position of editor in chief of LNSE. We encourage authors to submit papers concerning any branch of Software Engineering.

LNSE 2014 Vol.2(4): 360-364 IS4SN: 2301-3559
DOI: 10.7763/LNSE.2014.V2.150

Mining Semantic Classes of Image Databases

Zaher Al Aghbari
Abstract—This paper demonstrates an approach to mine semantic classes of image regions (e.g. sky, snow, rocks, etc.). Particularly, classify image regions based on their semantics using a support vector machines (SVMs). The SVMs learns the semantics of specified classes from specific low-level feature of the test image regions. Image regions are, first, segmented using a hill-climbing approach. Then, those regions are classified by the SVMs. Such semantic classification allows the implementation of intuitive query interface. In these experiments, the high precision of semantic classification justifies the feasibility of our approach.

Index Terms—Image mining, image classification, hill-climbing, semantic classification.

Zaher Al Aghbari is with the Department of Computer Science, University of Sharjah, UAE (e-mail: zaher@sharjah.ac.ae).


Cite: Zaher Al Aghbari, "Mining Semantic Classes of Image Databases," Lecture Notes on Software Engineering vol. 2, no. 4, pp. 360-364, 2014.

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