Exploration of Visual Data presents latest research efforts in the
area of content-based exploration of image and video data. The main
objective is to bridge the semantic gap between high-level concepts in
the human mind and low-level features extractable by the machines.
The two key issues emphasized are "content-awareness" and
"user-in-the-loop". The authors provide a comprehensive review on
algorithms for visual feature extraction based on color, texture, shape,
and structure, and techniques for incorporating such information to aid
browsing, exploration, search, and streaming of image and video data.
They also discuss issues related to the mixed use of textual and
low-level visual features to facilitate more effective access of
multimedia data.
To bridge the semantic gap, significant recent research efforts have
also been put on learning during user interactions, which is also known
as "relevance feedback". The difficulty and challenge also come from the
personalized information need of each user and a small amount of
feedbacks the machine could obtain through real-time user interaction.
The authors present and discuss several recently proposed classification
and learning techniques that are specifically designed for this problem,
with kernel- and boosting-based approaches for nonlinear extensions.
Exploration of Visual Data provides state-of-the-art materials on
the topics of content-based description of visual data, content-based
low-bitrate video streaming, and latest asymmetric and nonlinear
relevance feedback algorithms, which to date are unpublished.
Exploration of Visual Data will be of interest to researchers,
practitioners, and graduate-level students in the areas of multimedia
information systems, multimedia databases, computer vision, machine
learning.
