论文标题
卷积神经网络和多级线性辨别分析的食物分类
Food Classification with Convolutional Neural Networks and Multi-Class Linear Discernment Analysis
论文作者
论文摘要
卷积神经网络（CNN）已经成功地表示在人脑中看到的完全连接的推论能力：它们充分利用了复杂数据中常见的层次结构式模式，并使用简单特征开发出更多模式。无数CNN的实现表明，他们学习这些复杂模式的能力有多强，尤其是在图像分类领域。但是，将高性能CNN达到所谓的“最新水平”水平的成本在计算上是昂贵的。即使使用转移学习（使用MobilenetV2等模型的深层层），CNN仍然需要大量时间和资源。线性判别分析（LDA）是Fisher线性判别的概括，可以以多类分类方法实现，以提高类特征的分离性，同时不需要高性能系统来进行图像分类。同样，我们也相信LDA在表现良好方面都有很大的希望。在本文中，我们讨论了开发用于食品分类的强大CNN的过程，以及我们有效实施多级LDA的过程，并证明（1）CNN优于LDA进行图像分类，以及（2）为什么不应将LDA排除在竞赛中以进行图像分类，尤其是二进制案例。
Convolutional neural networks (CNNs) have been successful in representing the fully-connected inferencing ability perceived to be seen in the human brain: they take full advantage of the hierarchy-style patterns commonly seen in complex data and develop more patterns using simple features. Countless implementations of CNNs have shown how strong their ability is to learn these complex patterns, particularly in the realm of image classification. However, the cost of getting a high performance CNN to a so-called "state of the art" level is computationally costly. Even when using transfer learning, which utilize the very deep layers from models such as MobileNetV2, CNNs still take a great amount of time and resources. Linear discriminant analysis (LDA), a generalization of Fisher's linear discriminant, can be implemented in a multi-class classification method to increase separability of class features while not needing a high performance system to do so for image classification. Similarly, we also believe LDA has great promise in performing well. In this paper, we discuss our process of developing a robust CNN for food classification as well as our effective implementation of multi-class LDA and prove that (1) CNN is superior to LDA for image classification and (2) why LDA should not be left out of the races for image classification, particularly for binary cases.
