NDLI: Improving Texture Based Classification of Aerial Images by Fractal Features

Content Provider	IEEE Xplore Digital Library
Author	Popescu, D. Ichim, L. Angelescu, N. Ionita, M.G.
Copyright Year	2015
Description	Author affiliation: Fac. of Autom. Control & Comput., Politeh. Univ. of Bucharest, Bucharest, Romania (Popescu, D.; Ichim, L.) \|\| Fac. of Electr. Eng., Electron. & Inf. Technol., Valahia Univ. of Targoviste, Targoviste, Romania (Angelescu, N.; Ionita, M.G.)
Abstract	In this paper we propose an effective method of aerial image classification, which combines three types of features: color-based, statistical and fractal information. Two distinct phases were necessary for the CBIR system, which includes the classification algorithm: the learning phase and the classification phase. In the learning phase 5 different and efficient features were selected: entropy, contrast, homogeneity, mass fractal dimension and lacunarity. Also, three categories (classes) in CBIR were considered. The method of comparison, based on sub-images, improves the texture-based classification. A set of 100 aerial images from UAV was tested for establishing the rate of classification. The rate of 96% accurate classification, obtained as result, confirms the efficiency of the proposed method.
Starting Page	578
Ending Page	583
File Size	732949
Page Count	6
File Format	PDF
e-ISBN	9781479917808
DOI	10.1109/CSCS.2015.18
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-05-27
Publisher Place	Romania
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Content based image retrieval Texture analysis Image color analysis Image retrieval Prototypes Fractal analysis Feature extraction Fractals Classification algorithms Feature extractions Image classification
Content Type	Text
Resource Type	Article

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