<html>
  <head>
    <meta content="text/html; charset=utf-8" http-equiv="Content-Type">
  </head>
  <body bgcolor="#FFFFFF" text="#000000">
    <u><font size="+1">Please, note the publication of the following
        CVIU special issue:</font></u><br>
    <br>
    <b>Image and Video Understanding in Big Data </b><b><br>
    </b><b> </b>Volume 156, Pages 1-186, March 2017<br>
    <b>Edited by: <br>
    </b>Vittorio Murino, Shaogang Gong, Chen Change Loy and Loris
    Bazzani<br>
    <br>
    More info, the Editorial (<a
href="http://personal.ie.cuhk.edu.hk/%7Eccloy/cviu_si/files/cviu_si_editorial.pdf">link</a>)
    and the list of papers are provided in the following.<br>
    <br>
    ==========================================================<br>
    <div class="span9">
      <h1> <font color="#9E0011">Computer Vision and Image
          Understanding</font> <br>
        Special Issue on Image and Video Understanding in Big Data</h1>
    </div>
    <h3>About This Special Issue </h3>
    <div class="row">
      <div class="span12">
        <p> The big data era has brought with it new challenges to
          computer vision and image understanding. More scalable and
          robust methods are required to efficiently index, retrieve,
          organize and interact with big visual data. One can only think
          to the amount of image/video data downloaded every minute in
          social media or to the number of surveillance cameras
          installed in our cities nowadays. Both cases are not
          manageable without automatic or semi-automatic (e.g.,
          human-in-the-loop) approaches capable of distill useful
          information from a large quantity of raw data. This special
          issue covers a wide range of topics, with a common denominator
          devoted to the analysis for understanding of images and
          videos. Some of these papers are extended versions of the best
          works presented at the International Conference on Image
          Analysis and Processing <a href="http://www.iciap2015.eu/">ICIAP
            2015</a>, held in Genova in September 2015. <br>
          <br>
        </p>
      </div>
    </div>
    <h3>Access </h3>
    <div class="row">
      <div class="span12">
        <p> The issue is available electronically on Science Direct at
          the following link: <a
            href="http://www.sciencedirect.com/science/journal/10773142/156">http://www.sciencedirect.com/science/journal/10773142/156</a>
          <br>
          <br>
        </p>
      </div>
    </div>
    <h3>Table of Contents </h3>
    <div class="row">
      <div class="span12">
        <ol>
          <li><span class="title"><strong> Image and Video Understanding
                in Big Data </strong></span><br>
            <span class="details"> Vittorio Murino, Shaogang Gong, Chen
              Change Loy, Loris Bazzani </span><br>
            <a
href="http://personal.ie.cuhk.edu.hk/%7Eccloy/cviu_si/files/cviu_si_editorial.pdf"><span
                class="label_download">PDF</span></a> </li>
          <br>
          <li><span class="title"><strong> MEG: Texture Operators for
                Multi-Expert Gender Classification </strong></span><br>
            <span class="details"> Modesto CastrilloĢn Santana, Maria De
              Marsico, Michele Nappi, Daniel Riccio </span></li>
          <br>
          <li><span class="title"><strong> Boosting Hankel Matrices for
                Face Emotion Recognition and Pain Detection </strong></span><br>
            <span class="details"> Liliana Lo Presti, Marco La Cascia </span></li>
          <br>
          <li><span class="title"><strong> Social Profiling through
                Image Understanding: Personality Inference using
                Convolutional Neural Networks </strong></span><br>
            <span class="details"> Cristina Segalin, Dong Seon Cheng,
              Marco Cristani </span></li>
          <br>
          <li><span class="title"><strong> Structured Learning of Metric
                Ensembles with Application to Person Re-Identification </strong></span><br>
            <span class="details"> Sakrapee Paisitkriangkrai, Lin Wu,
              Chunhua Shen, Anton van den Hengel </span></li>
          <br>
          <li><span class="title"><strong> Continuous Adaptation of
                Multi-Camera Person Identification Models through Sparse
                Non-redundant Representative Selection </strong></span><br>
            <span class="details"> Abir Das, Rameswar Panda, Amit K
              Roy-Chowdhury </span></li>
          <br>
          <li><span class="title"><strong> A Bag-of-Words Equivalent
                Recurrent Neural Network for Action Recognition </strong></span><br>
            <span class="details"> Alexander Richard, Juergen Gall </span></li>
          <br>
          <li><span class="title"><strong> Improved Scene Identification
                and Object Detection on Egocentric Vision of Daily
                Activities </strong></span><br>
            <span class="details"> Gonzalo Vaca-Castano, Samarjit Das,
              Joao P Sousa, Niels D. Lobo, Mubarak Shah </span></li>
          <br>
          <li><span class="title"><strong> Fast Action Retrieval from
                Videos via Feature Disaggregation </strong></span><br>
            <span class="details"> Jie Qin, Li Liu, Mengyang Yu, Yunhong
              Wang, Ling Shao </span></li>
          <br>
          <li><span class="title"><strong> Detecting Anomalous Events in
                Videos by Learning Deep Representations of Appearance
                and Motion </strong></span><br>
            <span class="details"> Dan Xu, Yan Yan, Elisa Ricci, Nicu
              Sebe </span></li>
          <br>
          <li><span class="title"><strong> Deep Active Object
                Recognition by Joint Label and Action Prediction </strong></span><br>
            <span class="details"> Mohsen Malmir, Karan Sikka, Deborah
              Forster, Ian Fasel, Javier R Movellan, Garrison W Cottrell
            </span></li>
          <br>
          <li><span class="title"><strong> Weak Supervision for
                Detecting Object Classes from Activities </strong></span><br>
            <span class="details"> Abhilash Srikantha, Juergen Gall </span></li>
          <br>
          <li><span class="title"><strong> Efficient Large-Scale
                Multi-Class Image Classification By Learning Balanced
                Trees </strong></span><br>
            <span class="details"> Tien-Dung Mai, Thanh Duc Ngo,
              Duy-Dinh Le, Duc Anh Duong, Kiem Hoang, Shin'ichi Satoh </span></li>
          <br>
          <li><span class="title"><strong> Online Supervised Hashing </strong></span><br>
            <span class="details"> Fatih Cakir, Sarah A Bargal, Stan
              Sclaroff </span></li>
          <br>
          <li><span class="title"><strong> Scalable Greedy Algorithms
                for Transfer Learning </strong></span><br>
            <span class="details"> Ilja Kuzborskij, Francesco Orabona,
              Barbara Caputo </span></li>
          <br>
        </ol>
      </div>
    </div>
    <h3>Guest Editors </h3>
    <a href="http://profs.sci.univr.it/%7Eswan/">Vittorio Murino</a>,
    Istituto Italiano di Tecnologia, Genova/University of Verona,
    Verona, Italy <br>
    <a href="http://www.eecs.qmul.ac.uk/%7Esgg/">Shaogang Gong</a>,
    Queen Mary University of London, UK <br>
    <a href="http://personal.ie.cuhk.edu.hk/%7Eccloy/">Chen Change Loy</a>,
    The Chinese University of Hong Kong, China <br>
    <a href="http://www.lorisbazzani.info/">Loris Bazzani</a><br>
    <br>
    ==========================================================<br>
    <pre class="moz-signature" cols="72">-- 
Vittorio Murino

*******************************************
Prof. Vittorio Murino, Ph.D.
Director
PAVIS - Pattern Analysis & Computer Vision

IIT Istituto Italiano di Tecnologia
Via Morego 30
16163 Genova, Italy

Phone:  +39 010 71781 504
Mobile: +39 329 6508554
Fax: +39 010 71781 236
E-mail: <a class="moz-txt-link-abbreviated" href="mailto:vittorio.murino@iit.it">vittorio.murino@iit.it</a>

Secretary: Sara Curreli 
email:  <a class="moz-txt-link-abbreviated" href="mailto:sara.curreli@iit.it">sara.curreli@iit.it</a>
Phone:  +39 010 71781 917

<a class="moz-txt-link-freetext" href="http://www.iit.it/pavis">http://www.iit.it/pavis</a>
********************************************</pre>
  </body>
</html>