icmr2015/tutorial_multimedia_quality_modeling.php at master · xiw9/icmr2015 · GitHub

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<!DOCTYPE html>
<html lang="en">
<head>
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  <title>Tutorial on Multimedia Quality Modeling | ACM ICMR 2015 | Shanghai, China</title>
</head>
<body>
  <?php include 'php/navbar.php' ?>
  <div class="ord-header">
    <img class="img-responsive" src="img/header.jpg" alt="Scenery of Shanghai">
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  <div class="container">
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      <div class="col-md-9">
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          <div class="panel-heading">
            <h1 class="panel-title">Tutorial on Multimedia Quality Modeling: Theories and Applications (Half Day)</h1>
          </div>
          <div class="panel-body" >
            <h3>Tutorial Description</h3>
            <p>Recently we have witnessed an explosive growth of image/video/audio data in both
the local centers and social-networking websites, such as Flickr, YouTube, and
Facebook. Artificial intelligence techniques have proven useful for interpreting this
preponderance of data. In the last decades, many quality models have been proposed.
Computational quality models evaluate multimedia contents either objectively or
subjectively, based on which large-scale multimedia content can be managed efficiently.
They are useful tools in various applications such as multimedia retrieval,
recommendation systems, graphical design, and etc. Building a successful quality
model depends on a wide range of domain knowledge, such as multimedia, computer
vision, machine learning, and even cognitive science. Extensive research efforts have
been dedicated to design multimedia quality models. While effective methods to
manipulate this task are still at their infancy. In detail, some key technical challenges are:
1) the deemphasized role of semantic content that are many times more important than
low-level visual features in media quality prediction; 2) the necessity to incorporate
human perception of multimedia contents (e.g., biologically-inspired visual/acoustic
features) for quality assessment; 3) the difficulty to optimally fuse low-level and high-
level visual features into a quality model; and 4) the lack of publicly available data sets
to fairly evaluate the performance of a specific quality model. This tutorial targets the
recent technical theory and applications on computational models, such as photo/video
quality-based retargeting and feature selection for multimedia retrieval. A brief outline
of our tutorial can be described as follows:</p>
<ul>
  <li>New computational models for image and video quality evaluation (by Luming Zhang);</li>
  <li>Applications closely related to computational quality models, such as image
cropping/retargeting, video summarization, and feature selection for efficient
multimedia retrieval (by Luming Zhang);</li>
  <li>Discovering biologically/psychologically-inspired visual features for computational
quality models (by Luming Zhang);</li>
  <li>Novel feature selection algorithms for multimedia analysis (by Yi Yang);</li>
  <li>State-of-the-art feature engineering techniques for multimedia event detection
(by Yi Yang);</li>
  </ul>
  <h3>Biographies of Organizers</h3>
  <p><b>Luming Zhang</b> received his Ph.D. degree in computer science from
Zhejiang University, China. Currently he is a Postdoc Senior Research
Fellow at the School of Computing, National University of Singapore.
His research interests mainly include multimedia analysis, image
enhancement, and pattern recognition. He has authored and co-
authored more than 40 scientific articles at top venues including IEEE
T-IP, T-MM, T-CYB, CVPR, and ACM MM. He served/is serving as
the Guest editor for nine international journals. He served as the PC
members of international conferences such as ACM Multimedia,
ICME, and ICMR. He is the associate editor of Neurocomputing and
KSII Transactions on Internet and Information Systems.</p>
<p><b>Yi Yang</b> is a Senior lecturer of Computer Science with the Centre for
Quantum Computation & Intelligent Systems, University of Technology,
Sydney. Prior to that, he was a postdoc research fellow with the school of
computer science, Carnegie Mellon University. He received the PhD
degree in Computer Science from Zhejiang University in 2010. His
research interest includes machine learning and its application to
computer vision and multimedia analysis.</p>
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