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http://dx.doi.org/10.3837/tiis.2019.04.019

Dual-Encoded Features from Both Spatial and Curvelet Domains for Image Smoke Recognition  

Yuan, Feiniu (College of Information, Mechanical and Electrical Engineering, Shanghai Normal University)
Tang, Tiantian (School of Communications and Electronics, Jiangxi Science and Technology Normal University)
Xia, Xue (School of Information Technology, Jiangxi University of Finance and Economics)
Shi, Jinting (Vocational School of Teachers and Technology, Jiangxi Agricultural University)
Li, Shuying (School of Automation, Xi'an University of Posts & Telecommunications)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.13, no.4, 2019 , pp. 2078-2093 More about this Journal
Abstract
Visual smoke recognition is a challenging task due to large variations in shape, texture and color of smoke. To improve performance, we propose a novel smoke recognition method by combining dual-encoded features that are extracted from both spatial and Curvelet domains. A Curvelet transform is used to filter an image to generate fifty sub-images of Curvelet coefficients. Then we extract Local Binary Pattern (LBP) maps from these coefficient maps and aggregate histograms of these LBP maps to produce a histogram map. Afterwards, we encode the histogram map again to generate Dual-encoded Local Binary Patterns (Dual-LBP). Histograms of Dual-LBPs from Curvelet domain and Completed Local Binary Patterns (CLBP) from spatial domain are concatenated to form the feature for smoke recognition. Finally, we adopt Gaussian Kernel Optimization (GKO) algorithm to search the optimal kernel parameters of Support Vector Machine (SVM) for further improvement of classification accuracy. Experimental results demonstrate that our method can extract effective and reasonable features of smoke images, and achieve good classification accuracy.
Keywords
Curvelet Transform; Dual-encoded Local Binary Pattern (Dual-LBP); Completed Local Binary Pattern (CLBP); Gaussian Kernel Optimization (GKO); Smoke Recognition;
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Times Cited By KSCI : 1  (Citation Analysis)
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