Dr. Xueying Hu Profile

Dr. Xueying Hu

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Proceedings Article | 8 March 2018 Paper

A novel approach for fire recognition using hybrid features and manifold learning-based classifier

Rong Zhu, Xueying Hu, Jiajun Tang, Sheng Hu

Proceedings Volume 10609, 106090H (2018) https://doi.org/10.1117/12.2283486

KEYWORDS: Visualization, Feature extraction, Image processing, Flame detectors, Detection and tracking algorithms, Image segmentation, Distance measurement, Associative arrays, RGB color model, Evolutionary algorithms

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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 nonoverlapping 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.

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