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面向烟雾识别与纹理分类的Gabor网络(ChinaMM2018)

袁非牛,夏雪,李钢,章琳,史劲亭(江西财经大学)

摘 要
目的:通过烟雾检测能够实现早期火灾预警,但烟雾的形状、色彩等属性对环境的变化敏感,这使烟雾特征容易缺乏辨别力与鲁棒性,最终导致图像烟雾识别的误报率与错误率较高。为解决以上问题,本文提出一种基于Gabor滤波的层级结构,可视为Gabor网络。方法:首先,构建一个Gabor卷积单元,包括基于Gabor的多尺度多方向局部响应提取和跨通道的响应浓缩。其次,将Gabor卷积单元输出的浓缩响应图进行跨通道编码并统计为直方图特征。以上Gabor卷积单元与编码层构成了一个Gabor基础层,对基础层引入最大响应编码和浓缩图全局优化能生成扩展特征。最后,将基础和扩展特征首尾相连形成鲁棒性更强的烟雾特征。通过堆叠上述Gabor基础层能形成一个前馈网络结构,将每一层特征首尾相连即可获得烟雾的多层级特征。结果:实验结果表明,此Gabor网络泛化性能好,所提烟雾特征的辨别力在对比实验中综合排名第一,所提纹理特征的辨别力排名在两个纹理数据集上分别排第一与第二。结论:所提Gabor网络能够实现多尺度、多方向的多层级纹理特征表达,能够提高烟雾识别的综合效果,也可提高纹理分类的准确率。未来可进一步研究如何降低特征的冗余度,探索不同层特征的关系并加以利用,以期在视频烟雾实时识别中得到实际应用。
关键词
A GaborNet for Smoke Recognition and Texture Classification

Yuan Fei niu,Xia Xue,Li Gang,Zhang Lin,Shi Jinting()

Abstract
Objective Since smoke often occurs earlier than flame when fire breaks out, smoke detection provides earlier fire alarms than flame detection does. The color, shape and movement of smoke are susceptible to external environment, so existing smoke features lack discriminative ability and robustness. These factors make image-based smoke recognition a difficult task. In order to decrease false alarm rates (FARs) and error rates (ERRs) of smoke recognition without dropping detection rates (DRs), we propose a Gabor-based hierarchy (GaborNet) in this paper. Method First, a Gabor convolutional unit, which consists of a set of learning-free convolutional filters and condensing modules, is constructed. The Gabor filters with fixed parameters generate a set of response maps from an original image as a multi-scale and multi-orientation representation. Besides, the condensing module conducts max-pooling across channels of every response map to further capture condensed and scale-invariant information. Then, condensed response maps, i.e. the outputs of the above-mentioned Gabor convolution unit, are encoded both within and across channels. LBP (Local Binary Pattern) encoding method is leveraged to describe texture distribution in every channel of a condensed map, and Hash binary encoding is used to capture the relations across map channels. The binarization in encoding module helps the representation be robust to local changes. Thereafter, histogram calculation is applied to encoded maps to obtain statistical features, known as basic features. The aforementioned Gabor convolution unit, including encoding module and histogram calculation, forms a basic Gabor layer. In addition, this Gabor layer is provided with two extensive modules. One is to futher explore the invariance of texture distributions, and the other is to enrich the pattern of response maps. The former restores and encodes the indices of max responses in the Gabor convolutional unit. The latter holisticly learns a set of projection vectors from condensed response maps to construct a feature space. Once being projected into this feature space, the texture representation not only becomes more seperable, but also carries more patterns. The extensive features improve the robustness and discriminative ability of basic features since holistic information and more patterns are characterized. At last, smoke features of a Gabor layer are generated by concatenating basic features and extensive ones. Through stacking several Gabor layers on top of each other, a feedforwad network, termed GaborNet, can be built. Consequently, the concatenation of features acquired from every Gabor layer constitutes multi-scale, multi-orientation and hierachical features. As a network goes deeper, the features becomes more high-level and less explicable. Thus, the extension, which explicitly improves basic features, is conducted only on the first Gabor layer that carries low-level features. Besides, once holistic learning in extension is implemented, this step is no required any more in subsequent steps. Result This paper conducted ablation experiments to gain insights to the extensive features. Then, comparison experiments for smoke recognition were carried out to present the performance of the proposed GaborNet. Since this algorithm utilizes texture representations to present smoke, texture classification was also conducted as a supplement to the experiment. Experimental results demonstrate that the proposed GaborNet achieves powerful generalization ability. Smoke features extracted by the GaborNet descrease false alarm rates and error rates without dropping detection rates, thus the result of GaborNet ranks first among state-of-the-art methods. While results of texture classfication rank at first and second place respectively in two widely-used texture datasets. In summary, the GaborNet provides better texture representation than most of the existing texture descriptors in both smoke recognition and texture classification. Conclusion The proposed GaborNet can extract multi-scale, multi-orientation and hierachical representations for textures, and consequently helps improve the performance of smoke recognition and increases the accuracy of texture classification. Future researches should focus on eliminating the redundancy in features to gain compactness, and on exploring and utilizing the relations between features in different layers to enchance transform invariance. Eventually, this method is expected to be practically applied in real-time video smoke recognition.
Keywords
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