The performance of image classification is highly dependent on the quality of the extracted features that are used to build a model. Designing such features usually requires prior knowledge of the domain and is often undertaken by a domain expert who, if available, is very costly to employ. Automating the process of designing such features can largely reduce the cost and efforts associated with this task. Image descriptors, such as local binary patterns, have emerged in computer vision, and aim at detecting keypoints, for example, corners, line-segments, and shapes, in an image and extracting features from those keypoints. In this article, genetic programming (GP) is used to automatically evolve an image descriptor using only two instances per class by utilising a multitree program representation. The automatically evolved descriptor operates directly on the raw pixel values of an image and generates the corresponding feature vector. Seven well-known datasets were adapted to the few-shot setting and used to assess the performance of the proposed method and compared against six handcrafted and one evolutionary computation-based image descriptor as well as three convolutional neural network (CNN) based methods. The experimental results show that the new method has significantly outperformed the competitor image descriptors and CNN-based methods. Furthermore, different patterns have been identified from analysing the evolved programs.

图像分类的性能高度依赖于用于构建模型的提取特征的质量。设计此类功能通常需要该领域的先验知识，并且通常由领域专家承担，如果有的话，聘用该专家的成本非常高。自动化设计此类功能的过程可以在很大程度上减少与此任务相关的成本和工作量。图像描述符，例如局部二进制模式，已经出现在计算机视觉中，旨在检测图像中的关键点，例如角点、线段和形状，并从这些关键点中提取特征。在本文中，遗传编程 (GP) 用于通过利用多树程序表示，每个类仅使用两个实例来自动演化图像描述符。自动进化的描述符直接对图像的原始像素值进行操作并生成相应的特征向量。七个众所周知的数据集适用于少镜头设置，用于评估所提出方法的性能，并与六种手工制作和一种基于进化计算的图像描述符以及三种基于卷积神经网络 (CNN) 的方法进行比较。实验结果表明，新方法明显优于竞争对手的图像描述符和基于 CNN 的方法。此外，通过分析进化的程序已经确定了不同的模式。七个众所周知的数据集适用于少镜头设置，用于评估所提出方法的性能，并与六种手工制作和一种基于进化计算的图像描述符以及三种基于卷积神经网络 (CNN) 的方法进行比较。实验结果表明，新方法明显优于竞争对手的图像描述符和基于 CNN 的方法。此外，通过分析进化的程序已经确定了不同的模式。七个众所周知的数据集适用于少镜头设置，用于评估所提出方法的性能，并与六种手工制作和一种基于进化计算的图像描述符以及三种基于卷积神经网络 (CNN) 的方法进行比较。实验结果表明，新方法明显优于竞争对手的图像描述符和基于 CNN 的方法。此外，通过分析进化的程序已经确定了不同的模式。