Institutional Repository
Technical University of Crete
Technical University of Crete
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| URI: | http://purl.tuc.gr/dl/dias/360BF0BC-1BB4-44E5-A3DE-A5B855B4E70B | ||
| Year | 2005 | ||
| Type of Item | Peer-Reviewed Journal Publication | ||
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| Bibliographic Citation | S. G. Mougiakakou ,A. Tsouchlaraki ,C. Cassios ,G. Matsopoulos ,K.S. Nikita ,N. Uzunoglou , “Scapeviewer : preliminary results of a landscape perception classification system based on neural network technology”, Ecol. Engineering, vol.24, no. 1-2, pp.5-15,2005.doi: 10.1016/j.ecoleng.2004.12.003 https://doi.org/10.1016/j.ecoleng.2004.12.003 | ||
| Appears in Collections |
In this paper, the implementation of a pilot computerized system for the classification of landscape images (SCAPEVIEWER) is presented. A total of 108 landscape photographs have been organized, according to the mean estimation of scenic beauty from seven experts, into three classes: indistinctive (C1), typical or common (C2), and distinctive (C3). For each of the landscape photographs, 10 indices are estimated. These indices are then fed to a classifier based on neural network (NN) technology. In order to examine whether NNs are suitable for this specific application, two different approaches have been tested and compared against a linear discrimination method (LDM) classifier. The first approach is a feed forward NN (Classic-NN), while the second approach (Hybrid-NN) is based on the Classic-NN modified by using genetic algorithms (GAs). The correct classification performances achieved by the Classic-NN and the Hybrid-NN were 87% and 84%, respectively, while the classification performance of the LDM classifier was only 68%. Although the Classic-NN achieved slightly better results than the Hybrid-NN, the latter is preferred due to its ability of index selection and automatical adjustment of internal NN parameters. The pilot system has shown the feasibility for classifying landscape photographs according to scenic beauty by means of a computerized system combining the knowledge of an expert with a NN classifier.
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