Fourth International Conference

on

Computer Vision & Image Processing

Organized by

Malaviya National Institute of Technology Jaipur

27 - 29 September 2019

Workshop on Machine Learning in Multimedia Signal Processing (MLMSP)


(Workshop Co-Chairs: Andrea KUTICS & Akihiko NAKAGAWA, International Christian University, Japan)

Machine learning techniques and deep learning algorithms have claimed very good performances on a wide range of tasks in even on Big Multimedia data. The expansion of online multimedia and other social media appearing on mobile, wearable and other devices has become extremely accelerated in recent years. The Machine Learning in Multimedia Signal Processing (MLMSP) workshop is based on emerging interdisciplinary multimedia research and systems using Machine-learning techniques on conventional and big multimedia data. The primary goal of the MLMSP workshop in CVIP 2019 is to present state-of-the art research results on emerging machine learning techniques in multimedia signal processing as well as their applications and services for retrieval, classification, semantics, detection and recognition of multimedia information including various signals, images, video and 3D/VR/AR/MR. The workshop also welcomes new topics that can help to establish semantical and contextual relations among users based on the information of their interest. We hope this goal will encourage academic and industrial interaction and to promote collaborative research activities on the field Machine Learning in Multimedia Signal Processing.
To download the call for paper Click Here
  • Deep and Machine Learning Models and Techniques
  • Novel machine and deep learning
  • Active learning
  • Incremental learning and online learning
  • Agent-based learning
  • Manifold learning
  • Multi-task learning / parallel and distributed learning
  • Bayesian networks and applications
  • Case-based reasoning methods
  • Statistical models and learning
  • Computational learning
  • Evolutionary algorithms and learning
  • Evolutionary neural networks
  • Fuzzy logic-based learning
  • Genetic optimization
  • Clustering, classification and regression
  • Neural network models and learning
  • Reinforcement learning
  • Supervised, semi-supervised and unsupervised learning
  • Multimedia Analysis and Processing
  • Novel multimedia signal processing and analysis
  • Content-based analysis for big multimedia data
  • Feature extraction for big multimedia data representation
  • Human activity recognition, action detection, motion tracking, and video surveillance
  • Multimedia search and retrieval
  • Semantics and emotion analysis
  • Computation linguistics analysis
  • Multimedia data modeling and visualization
  • Filtering, Time-Sensitive and Real-time Search
  • Personalized Search
  • Images and video data mining
  • Multimedia knowledge discovery in large datasets
  • Indexing, classification, clustering, and association
  • Segmentation, grouping and shape representation
  • Multimedia knowledge acquisition and learning
  • Multimedia knowledge representation and reasoning
  • Mining spatial and temporal multimedia datasets

  • Machine Learning Multimedia Applications
  • Retrieval and annotation of big multimedia data
  • Object and/or context based multimedia information retrieval
  • Multimedia networking, communication, and IoT
  • Emotion and semantics in content-based retrieval systems
  • Multi-modal multimedia systems, document processing
  • Multimedia image/video scene understanding
  • Semantic-based multimedia retrieval and annotation
  • Mobile Multimedia Systems and Applications
  • Cloud-assisted multimedia systems
  • Human computer interaction based on multimedia
  • Entertainment, gaming and e-learning
  • 3D / AR / VR / MR, Animation
  • Intelligent traffic and transportation
  • Multimedia security, rights management and forensics
  • Multimedia systems for digital library and SNS
  • Multimedia for smart homes
  • Multimedia for wearable technologies and applications
  • Bioinformatics, biomedical informatics, and face recognition
  • Medical, healthcare, medicine and clinical decision support
  • Computer vision
  • Natural language processing
  • Recommendation systems

Submission and publication


CVIP 2019 invites submission of high quality and original papers on the topics listed above. All submitted papers will be peer-reviewed by at least three reviewers for technical merit, originality, significance and relevance to track topics. Papers must be up to 12 pages and follow Springer Lecture Notes publication format. Accepted papers will be included in the conference proceedings and submitted for inclusion to IAPR and major indexes. Content will be submitted to the indexing companies for possible indexing.

Important Dates


Submission Deadline May 15, 2019 June 15,2019 [11:59 p.m. Indian Standard Time]
Supplementry Material Deadline May 20, 2019 June 20, 2019 [11:59 p.m. Indian Standard Time]
Final Decision To Author July 25, 2019 [11:59 p.m. Indian Standard Time]
Camera Ready Paper August 05, 2019 [11:59 p.m. Indian Standard Time]
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