AN ONTOLOGY-DRIVEN VEGETATION CLASSIFICATION FOR SEMANTIC-BASED IMAGE RETRIEVAL OF AERIAL PHOTOGRAPHY | [Quaid-e-Awam University Research Journal of Engineering, Science and Technology • 2016]

Author(s):

1. SAQIB MAJEED: University Institute of Information Technology, PMAS-Arid Agriculture University Rawalpindi, Pakistan

2. SALEEM IQBAL: University Institute of Information Technology, PMAS-Arid Agriculture University Rawalpindi, Pakistan

3. ADNAN AHMED: Department of Computer Systems Engineering, Quaid-e-Awam University of Engineering, Science and Technology,Nawabshah, Pakistan

4. KULSOOM AZAD: University Institute of Information Technology, PMAS-Arid Agriculture University Rawalpindi, Pakistan

5. KASHIF SATTAR: University Institute of Information Technology, PMAS-Arid Agriculture University Rawalpindi, Pakistan

Abstract:

The retrieval of images using semantic concepts is challenging and essential issue in watchword based search. The focus has been shifted from low-level feature extraction to human semantics in order to enhance the precision of content based image recovery framework. It is done to decrease the semantic gap between visual components and affluence of human semantics. This research provides an ontology-driven approach to train the system by modeling the human cognition that also conquers the constraints of a label based retrieval. In the proposed work, a properly characterised philosophy framework is used to drive image annotations that enable semantic retrieval of aerial photography. The concept of image semantics is exploited for the classification of vegetation and also to determine the qualitative semantics by using Allen’s qualitative relations. Psychophysical evaluation is used to assess the eectiveness of the proposed approach. The outcomes of the different experiments are relatively favorable in terms of accuracy of relevant image retrieval.

Page(s): 32-37

DOI: DOI not available

Published: Journal: Quaid-e-Awam University Research Journal of Engineering, Science and Technology, Volume: 15, Issue: 2, Year: 2016

Keywords:

Vegetation Classi cation , Qualitative Semantics , Aerial Photography , Semantic Image Retrieval

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