基于矩阵模式的林火图像半监督学习算法

Semi-Supervised Algorithm for Forest Fire Recognition Based on Matrix Pattern

森林火灾图像识别是森林防火监测系统的核心。目前的主要研究多在图像的向量模式表示上展开。由于向量模式的样本数由图像分辨率决定,易导致模型训练的负担过重。样本类别标记的准确性,直接影响后续的模型训练和目标识别。而目前的类别标定工作多采用手工或图像预处理方法完成,任务繁琐且容易出错。此外,由于像素位置在图像向量化过程中被调整,不可避免地会损失图像原有的结构信息。鉴于此,提出了基于矩阵分块的半监督学习算法Semi-MHKS,优势在于:①矩阵分块形式的样本数远低于向量模式,可有效缩短训练和识别时间;②只需标记分块类别,更有利于准确标定样本类别;③采用双线性判别函数,设计了针对林火问题的半监督学习算法;④证明了算法的收敛性。与支持向量机(SVM)、MHKS和半监督的LapMatLSSVM方法相比,在林火图像和视频上的实验验证了Semi-MHKS的具有较高的识别率和较低的训练时间。

Forest fire image recognition/detection plays a vital role in forest fire monitoring system. Due to its own characteristics and difficulties of forest fire image, the existing studies mainly focus on the vector-pattern-oriented fire image, where each vector-pattern sample corresponds to an image pixel one by one. Since the number of vector-pattern samples is strongly determined by the resolution of the given image, it is time-consuming for training classifier to deal with numerous vector-pattern samples, especially for higher-quality images. How to label samples is another big challenge in the task of image target recognition. However, at present, this labeling work is done manually or semi-manually(for instance, the method of image preprocessing). It is clear that the accuracy of labels directly affects subsequent steps including classifier training and object recognition. Furthermore, owing to the rearrangement of adjacency relationship between pixels, vector-pattern samples, which are generated from image pixel-by-pixel vectorization, unavoidably lost the original image structural information. In this paper, we proposed a matrix-pattern semi-supervised algorithm for forest fire image recognition, named Semi-MHKS(semi-supervised matrix-pattern Ho-Koshyap algorithm with squared approximation). Its advantages lie in 4 aspects:①Instead of vector-pattern, it adopts sub-matrix-pattern samples to train classifier. In doing so, it is more likely to meet real-time requirements because of smaller size of training set.②It is easier to label the training samples in the manner of sub-matrix-pattern than that of vector pattern. Moreover, it is also effective for decreasing the error rate in manual-labeling.③Adopting so-called bi-linear discriminant function, we design a semi-supervised learning algorithm(Semi-MHKS) for forest fire images, which only needs several labeled samples. It is also suitable for classifying the a batch of unknown matrix-pattern samples.④The algorithm leads to a strictly convex optimization problem, which can be solved by quadratic programming and gradient descend method. It is mathematically proved that Semi-MHKS is convergent in the stage of alternating iteration, with fixed left or right weight vectors of the bi-linear function. Compared to state-of-the-art methods, including vector-pattern support vector machine(SVM), matrix-pattern MHKS, and matrix-pattern semi-supervised LapMatLSSVM(Laplacian matrix-based least square SVM), the experiments on forest fire images verify that our proposed algorithm has higher fire image recognition rate and less training time.