The Second IEEE International Workshop on

Machine Learning and Computing for Visual Semantic Analysis (MLCSA)

 

Recently, visual 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 visual 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 semantic visual representation in numerous computer vision applications such as image classification, object detection/localization, image segmentation, captioning, and so on. With machine learning and computing 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 visual semantic analysis.

This workshop, on Machine Learning and Computing for Visual Semantic Analysis (MLCSA2018) – aims at sharing latest progress and developments, current challenges, and potential applications for exploiting large amounts of visual contents. We are interested in constructing effective systems to enable visual semantic analysis and building wide applications within the fields of artificial intelligence, machine learning, image processing, ubiquitous computing, data mining, and others.

 

Topics of Interest

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

  • Unsupervised and semi-supervised learning
  • Deep/transfer learning for image and multimedia analysis
  • Statistical modeling of image processing task
  • Spatio-temporal data mining
  • Feature extraction and matching
  • Activity/Pattern learning and recognition
  • Application of visual semantic analysis
  • Semantic analysis of surveillance image and video
  • Remote sensing image understanding
  • Big data management
  • Medical data analysis
  • Social data analysis

The workshop will be held in conjunction with ISM 2018 in Taichung, Taiwan on December 10-12, 2018. It solicits regular technical papers of up to 6 pages (IEEE double-column format). Workshop papers will be official publications of IEEE which will` be included in IEEEXplore and also be available as printed workshop proceedings.

 

Important Dates

  • Workshop Paper Submission: October 17, 2018
  • Acceptance Notification: October 24, 2018
  • Camera-Ready and Registration: October 31, 2018

 

Paper Submission Process

Papers submitted to the MLCSA workshop must not have been previously published and must not be currently under consideration for publication elsewhere. We invite researchers and practitioners to submit papers to the MLCSA workshop according to the guidelines available on the conference website at http://ism.asia.edu.tw/2018/. Only electronic submission will be accepted. Paper authors MUST submit their manuscripts in PDF through EasyChair submission system.
Paper Submission Site: https://easychair.org/conferences/?conf=mlcsa2018

 

Workshop Organizers

YongQing Sun, NTT Communications, Japan
Xian-Hua Han, Yamaguchi University, Japan
YuanYuan Wang, Yamaguchi University, Japan

 

Program Committee (Tentative)

Yen-Wei Chen, Ritsumeikan University, Japan
Wen-Huang Cheng, Academia Sinica, Taiwan
Basabi Chakraborty, Iwate Prefectural University, Japan
JunPing Deng, ShangHai Ocean University, China
Xin Fan, Dalian University of Technology, China
Yutaro Iwamoto, Ritsumeikan University, Japan
YanLi Ji, University of Electronic Science and Technology of China, China
YuGang Jiang, Fudan University, China
Akisato Kimura, NTT Communication Science Laboratories, Japan
Xu Qiao, Shandong University, China
Jia Su, Capital Normal University, China
JianDe Sun, ShanDong Normal University, China
BoXin Shi, National Institute of Advanced Industrial Science and Technology, Japan
Jian Wang, ShanDong Normal University, China