A Survey on Image Classification Techniques

Image classification is a complex and time-consuming process if performed manually, with the implementation of different image classification methods, the process can be automated to obtain a highly accurate result. This review paper delivers an understanding of various image classification methods, with an emphasis on the summarization of classification methods and the techniques used for improving classification accuracy. Besides, various classification methods, performance, advantages, and limitations are compared. This literature review describes different types of supervised, unsupervised, and semi-supervised classification methods such as convolutional neural network, transfer learning, support vector machine, k-nearest neighbor, and random forest algorithm.