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

• Feiniu Yuan,
• Tiantian Tang,
• Xue Xia,
• Jinting Shi,
• Shuying Li,

Vol. 13, No. 4, April 29, 2019
10.3837/tiis.2019.04.019, Download Paper (Free):

• Curvelet Transform
• Dual-encoded Local Binary Pattern (Dual-LBP)
• Completed Local Binary Pattern (CLBP)
• Gaussian Kernel Optimization (GKO)
• Smoke Recognition

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.

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