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摘要
Image classification is a hot topic in image processing. Image classification aims to automatically classify large numbers of images. Many methods have been proposed for solving this task. Traditional methods usually leverage low-level features. Clustering is the most commonly used method of image classification. In recent years, convolutional neural networks (CNNs) is widely used in extracting deep features. Many network architectures are proposed for image classification, such as ResNeXt, Cifar10. These deep learning methods aims at fusing features of texture, color and segmentation. In this paper, we discuss the different methods and techniques of image classification, and made a detailed summary of their performance. We believe that our work plays an important role in the field of image classification.
- 出版日期2019-2
- 单位平顶山学院; 合肥工业大学
