Multimedia Technology and Enhanced Learning: Second EAI International Conference, ICMTEL 2020, Leicester, UK, April 10-11, 2020, Proceedings, Part I

To identify tea categories more automatically and efficiently, we proposed an improved tea identification system based on Histogram Equalization (HE), Gray-Level Co-Occurrence Matrix (GLCM) and Support Vector Machine (SVM) algorithm. In our previous project, 25 images per class might be not enough to classify, and a small size of dataset will cause overfitting. Therefore, we collected 10 kinds of typical processed Chinese tea, photographed 300 images each category by Canon EOS 80D camera, and regarded them as a first-hand dataset. The dataset was randomly divided into training set and testing set, which both contain 1500 images. And we applied data augmentation methods to augment the training set to a 9000-image training set. All the images were resized to 256 * 256 pixels as the input of feature extraction process. We enhanced the image features through Histogram Equalization (HE) and extracted features from each image which were trained through Gray-Level CoOccurrence Matrix (GLCM). The results show that the average accuracy reached 94.64%. The proposed method is effective for tea identification process.