Small Convolutional Neural Network Trainer Designed through Transfer Learning in Dessert Classification

Abstract

This paper established a Convolutional Neural Network (CNN) with the concept of transfer learning, and combined the main feature analysis calculation and clustering algorithm to further demonstrate the superiority of the proposed small CNN trainer in the identification of traditional Kinmen desserts. Food dessert identification methods directly use skin texture, color, shape, and other features as the basis. This paper effectively extracted image features of an object by the small CNN trainer and classified the featured dataset into the right food categories. It was able to complete classification quickly and also achieved high-precision classification results. Different types of Kinmen desserts were identified through a multi-layer training cycle. A total of 100 training images for each of the 10 food categories and each image size is converted into a smaller training data set by capturing the important features through the CNN trainer. Then, the main features were generated and the dimensions of each food image data were reduced again by using the t-Distributed Stochastic Neighbor Embedding (t-SNE) or Principal Component Analysis (PCA) methods. An individually K-means or k-nearest neighbors (KNN) algorithms efficiently completed the grouping results and in the classified image restoration. The experimental results compared the classifications after the learning cycle of different trainers and showed that the highest accuracy that the appropriated CNN trainer of the proposed methology obtained was up to 99% with a minimum executing time of about 99.37 seconds.