Call for Papers

Recently, image contents collected from surveillance cameras, mobile phones, personal photo collections, news footage, or medical images have been explosively increased. How to automatically/quantitatively analyze and understand the acquired image contents is becoming one of the most active research areas in the vision community due to the scientifically challenging problems and its great benefits to real life applications. On the other hand, machine learning techniques especially the deep learning framework have manifested the surprising superiority for extracting structural and inherent representation in numerous computer vision applications such as image classification, object detection/localization, image segmentation, captioning, and so on. With machine learning techniques, it is prospected to discover the inherent structure of the available unconditioned visual contents and to achieve more promising results for various applications based on image analysis.

This workshop, on Machine Learning for Image Analysis and Understanding (MLIAU2019) ? aims at sharing latest progress and developments, current challenges, and potential applications for exploiting large amounts of image contents. We are interested in constructing effective systems to enable image analysis/understanding and building wide applications within the fields of artificial intelligence, machine learning, image processing, data mining, and others.

The topics of interest include, but are not limited to, the following:

This workshop will bring together machine learning and computer vision experts from academia, industry, and government who have made progress in developing machine learning tools for image analysis and understanding. It will provide a comprehensive forum on this topic and foster in-depth discussion of technical and application issues. It will also serve as an introduction to researchers and students curious about this important and fertile field.

·Deep/transfer learning for image and multimedia analysis

·Unsupervised and semi-supervised learning

·Feature extraction and matching

·Statistical modeling of image processing task

·Image classification, recognition and segmentation

·Object detection and localization

·Application of visual semantic analysis

·Semantic analysis of surveillance image and video

·Remote sensing image understanding

·Hyperspectral image analysis

·Big data analysis

·Medical image processing