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A method of evolving novel feature extraction algorithms for detecting buried objects in FLIR imagery using genetic programming

机译：一种进化新特征提取算法的遗传算法在FLIR图像中检测掩埋物体的方法

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In this paper we present an approach that uses Genetic Programming (GP) to evolve novel feature extraction algorithms for greyscale images. Our motivation is to create an automated method of building new feature extraction algorithms for images that are competitive with commonly used human-engineered features, such as Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG). The evolved feature extraction algorithms are functions defined over the image space, and each produces a real-valued feature vector of variable length. Each evolved feature extractor breaks up the given image into a set of cells centered on every pixel, performs evolved operations on each cell, and then combines the results of those operations for every cell using an evolved operator. Using this method, the algorithm is flexible enough to reproduce both LBP and HOG features. The dataset we use to train and test our approach consists of a large number of pre-segmented image "chips" taken from a Forward Looking Infrared Imagery (FLIR) camera mounted on the hood of a moving vehicle. The goal is to classify each image chip as either containing or not containing a buried object. To this end, we define the fitness of a candidate solution as the cross-fold validation accuracy of the features generated by said candidate solution when used in conjunction with a Support Vector Machine (SVM) classifier. In order to validate our approach, we compare the classification accuracy of an SVM trained using our evolved features with the accuracy of an SVM trained using mainstream feature extraction algorithms, including LBP and HOG.
机译：在本文中，我们提出了一种使用遗传编程（GP）来开发新颖的灰度图像特征提取算法的方法。我们的动机是创建一种自动方法，为与常用的人为特征（例如本地二值模式（LBP）和定向梯度直方图（HOG））具有竞争力的图像建立新的特征提取算法。进化的特征提取算法是在图像空间上定义的函数，并且每个算法都会产生可变长度的实值特征向量。每个演化特征提取器将给定图像分解为以每个像素为中心的一组单元，对每个单元执行演化操作，然后使用演化算子将这些操作的结果组合到每个单元中。使用此方法，该算法足够灵活，可以重现LBP和HOG特征。我们用来训练和测试我们方法的数据集包括大量预先分割的图像“芯片”，这些图像是从安装在行驶中的汽车引擎盖上的前视红外图像（FLIR）相机拍摄的。目的是将每个图像芯片分类为包含或不包含掩埋物体。为此，我们将候选解决方案的适用性定义为与支持向量机（SVM）分类器结合使用时，由所述候选解决方案生成的特征的交叉折叠验证准确性。为了验证我们的方法，我们将使用我们的进化特征训练的SVM的分类准确性与使用主流特征提取算法（包括LBP和HOG）训练的SVM的准确性进行比较。

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