Trends and Prospects in Image Classification and Machine Learning: Techniques and Applications

Dear Colleagues,

The volume of image data is growing exponentially due to the wide use of low-cost electronic devices around the world, and image data are becoming more dominant than traditional textual data. There is thus a pressing demand for autoprocessing tools to deal with the rapid growth in image data. Many revolutionary AI techniques also rely on efficient image understanding or classification tools to achieve practical usability, such as biometric, autodriving, AI doctors, robots, content-based image searching, etc. However, the processing of image data is very challenging. Despite decades of intensive research on image understanding, successful image classification remains an unsolved problem, and we are yet to find an image classifier as efficient as the human visual system. In recent years, there have been significant developments in image classification characterised by large scale training and deep learning. The aim of this Special Issue is to capture these latest developments as well as many of the latest machine learning algorithms and advanced image applications. Potential topics of interest for the Special Issue include but are not limited to the following:

• Recurrent convolutional neural networks;
• Residual convolutional neural networks;
• Deep autoencoder;
• Generative adversarial networks;
• Graph convolutional networks;
• Supervised learning;
• Unsupervised learning;
• Semi-supervised learning;
• Association rule learning;
• AI applications.

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• deep learning
• machine learning
• image classification
• convolutional neural networks