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首页> 外文会议>International symposium on multispectral image processing and pattern recognition >A Novel Approach for Fire Recognition Using Hybrid Features and Manifold Learning-Based Classifier
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A Novel Approach for Fire Recognition Using Hybrid Features and Manifold Learning-Based Classifier

机译：一种基于混合特征和基于流形学习的分类器的火灾识别新方法

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Although image/video based fire recognition has received growing attention, an efficient and robust fire detection strategy is rarely explored. In this paper, we propose a novel approach to automatically identify the flame or smoke regions in an image. It is composed to three stages: (1) a block processing is applied to divide an image into several non-overlapping image blocks, and these image blocks are identified as suspicious fire regions or not by using two color models and a color histogram-based similarity matching method in the HSV color space, (2) considering that compared to other information, the flame and smoke regions have significant visual characteristics, so that two kinds of image features are extracted for fire recognition, where local features are obtained based on the Scale Invariant Feature Transform (SIFT) descriptor and the Bags of Keypoints (BOK) technique, and texture features are extracted based on the Gray Level Co-occurrence Matrices (GLCM) and the Wavelet-based Analysis (WA) methods, and (3) a manifold learning-based classifier is constructed based on two image manifolds, which is designed via an improve Globular Neighborhood Locally Linear Embedding (GNLLE) algorithm, and the extracted hybrid features are used as input feature vectors to train the classifier, which is used to make decision for fire images or non fire images. Experiments and comparative analyses with four approaches are conducted on the collected image sets. The results show that the proposed approach is superior to the other ones in detecting fire and achieving a high recognition accuracy and a low error rate.
机译：尽管基于图像/视频的火灾识别已受到越来越多的关注，但很少探索一种有效而强大的火灾探测策略。在本文中，我们提出了一种新颖的方法来自动识别图像中的火焰或烟雾区域。它分为三个阶段：（1）进行块处理，将图像分为几个不重叠的图像块，并通过使用两个颜色模型和基于颜色直方图的方法将这些图像块识别为可疑着火区域HSV色彩空间中的相似度匹配方法，（2）考虑到与其他信息相比，火焰和烟雾区域具有明显的视觉特征，因此提取了两种图像特征进行火灾识别，其中基于该特征获得局部特征。尺度不变特征变换（SIFT）描述符和关键点袋（BOK）技术，以及基于灰度共生矩阵（GLCM）和基于小波分析（WA）方法提取纹理特征，以及（3）基于两个图像流形构建基于流形学习的分类器，通过改进的球形邻域局部线性嵌入（GNLLE）算法进行设计，提取的混合特征为用作输入特征向量来训练分类器，该分类器用于确定火灾图像或非火灾图像。对收集的图像集进行了四种方法的实验和比较分析。结果表明，该方法在检测火灾，识别精度高，错误率低等方面优于其他方法。

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