改进局部三值模式的烟雾识别和纹理分类

doi:10.3778/j.issn.1673-9418.1907036

计算机科学与探索 ›› 2020, Vol. 14 ›› Issue (7): 1221-1231.DOI: 10.3778/j.issn.1673-9418.1907036

改进局部三值模式的烟雾识别和纹理分类

李钢，袁非牛，夏雪，章琳，雷帮军

1. 江西财经大学信息管理学院，南昌 330032
2. 水电工程智能视觉监测湖北省重点实验室（三峡大学），湖北宜昌 443002
3. 宜春学院数学与计算机科学学院，江西宜春 336000
4. 上海师范大学信息与机电工程学院，上海 201418
5. 江西科技师范大学数学与计算机科学学院，南昌 330038

出版日期:2020-07-01 发布日期:2020-08-12

Smoke Recognition and Texture Classification Using Improved Local Ternary Patterns

LI Gang, YUAN Feiniu, XIA Xue, ZHANG Lin, LEI Bangjun

1. School of Information Management, Jiangxi University of Finance and Economics, Nanchang 330032, China
2. Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang, Hubei 443002, China
3. College of Mathematics and Computational Science, Yichun University, Yichun, Jiangxi 336000, China
4. College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 201418, China
5. School of Mathematics and Computer Science, Jiangxi Science and Technology Normal University, Nanchang 330038, China

Online:2020-07-01 Published:2020-08-12

摘要/Abstract

摘要：

为提高烟雾识别的检测率和降低误报率，首先提出了基于置信水平的局部三值模式（CLLTP），进而提出了基于CLLTP的组合特征模型（M_CLLTP）。CLLTP是依据差分图像的像素值呈正态分布而提出的一种改进的局部三值模式。M_CLLTP模型提取了原图的CLLTP特征、Gabor特征图的加权的CLLTP特征和边缘特征图的CLLTP特征，并融合它们生成M_CLLTP特征。对比实验显示，M_CLLTP方法在三个烟雾数据集上都获得了较高的检测率和[F1]分数、较低的误报率，在两个纹理数据库上获得了最高的平均召回率。实验结果表明，所提方法对烟雾和纹理具有很好的辨识能力，非常适用于烟雾识别。

关键词: 烟雾识别, 局部三值模式, 置信水平, Gabor变换, 边缘特征, 纹理分类

Abstract:

To improve detection rate and reduce false alarm rate for smoke recognition, this paper presents local ternary pattern based on confidence level (CLLTP), and further presents a novel multi-CLLTP (M_CLLTP) feature extraction model based on CLLTP. CLLTP is an improved local ternary pattern according to the normal distribution of the pixel values on the difference images. M_CLLTP model computes the CLLTPs for the original images, the weighted CLLTPs for the Gabor feature maps and the CLLTPs for the edge feature maps, and then concatenates the three CLLTP features to generate M_CLLTP feature. Comparative experiments show that M_CLLTP method achieves higher detection rates, lower false alarm rates and higher[F1]scores on three smoke datasets, and the highest mean recall rates on two texture databases. Experimental results indicate that the presented method has a good discriminative ability for smoke and texture, and is very suitable for smoke recognition.

Key words: smoke recognition, local ternary pattern, confidence level, Gabor transform, edge feature, texture classification

李钢，袁非牛，夏雪，章琳，雷帮军. 改进局部三值模式的烟雾识别和纹理分类[J]. 计算机科学与探索, 2020, 14(7): 1221-1231.

LI Gang, YUAN Feiniu, XIA Xue, ZHANG Lin, LEI Bangjun. Smoke Recognition and Texture Classification Using Improved Local Ternary Patterns[J]. Journal of Frontiers of Computer Science and Technology, 2020, 14(7): 1221-1231.

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改进局部三值模式的烟雾识别和纹理分类

Smoke Recognition and Texture Classification Using Improved Local Ternary Patterns

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