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This unique volume reviews the latest advances in domain adaptation
in the training of machine learning algorithms for visual
understanding, offering valuable insights from an international
selection of experts in the field. The text presents a diverse
selection of novel techniques, covering applications of object
recognition, face recognition, and action and event recognition.
Topics and features: reviews the domain adaptation-based machine
learning algorithms available for visual understanding, and
provides a deep metric learning approach; introduces a novel
unsupervised method for image-to-image translation, and a video
segment retrieval model that utilizes ensemble learning; proposes a
unique way to determine which dataset is most useful in the base
training, in order to improve the transferability of deep neural
networks; describes a quantitative method for estimating the
discrepancy between the source and target data to enhance image
classification performance; presents a technique for multi-modal
fusion that enhances facial action recognition, and a framework for
intuition learning in domain adaptation; examines an original
interpolation-based approach to address the issue of tracking model
degradation in correlation filter-based methods. This authoritative
work will serve as an invaluable reference for researchers and
practitioners interested in machine learning-based visual
recognition and understanding.
This unique volume reviews the latest advances in domain adaptation
in the training of machine learning algorithms for visual
understanding, offering valuable insights from an international
selection of experts in the field. The text presents a diverse
selection of novel techniques, covering applications of object
recognition, face recognition, and action and event recognition.
Topics and features: reviews the domain adaptation-based machine
learning algorithms available for visual understanding, and
provides a deep metric learning approach; introduces a novel
unsupervised method for image-to-image translation, and a video
segment retrieval model that utilizes ensemble learning; proposes a
unique way to determine which dataset is most useful in the base
training, in order to improve the transferability of deep neural
networks; describes a quantitative method for estimating the
discrepancy between the source and target data to enhance image
classification performance; presents a technique for multi-modal
fusion that enhances facial action recognition, and a framework for
intuition learning in domain adaptation; examines an original
interpolation-based approach to address the issue of tracking model
degradation in correlation filter-based methods. This authoritative
work will serve as an invaluable reference for researchers and
practitioners interested in machine learning-based visual
recognition and understanding.
The book is a collection of best selected research papers presented
at 6th International Conference on Innovations in Electronics and
Communication Engineering at Guru Nanak Institutions Hyderabad,
India. The book presents works from researchers, technocrats and
experts about latest technologies in electronic and communication
engineering. The book covers various streams of communication
engineering like signal processing, VLSI design, embedded systems,
wireless communications, and electronics and communications in
general. The authors have discussed the latest cutting edge
technology and the volume will serve as a reference for young
researchers.
Compressed sensing or compressive sensing is a new concept in
signal processing where one measures a small number of non-adaptive
linear combinations of the signal. These measurements are usually
much smaller than the number of samples that define the signal.
From these small numbers of measurements, the signal is then
reconstructed by non-linear procedure. Compressed sensing has
recently emerged as a powerful tool for efficiently processing data
in non-traditional ways. In this book, we highlight some of the key
mathematical insights underlying sparse representation and
compressed sensing and illustrate the role of these theories in
classical vision, imaging and biometrics problems.
This book provides an overview of different deep learning-based
methods for face recognition and related problems. Specifically,
the authors present methods based on autoencoders, restricted
Boltzmann machines, and deep convolutional neural networks for face
detection, localization, tracking, recognition, etc. The authors
also discuss merits and drawbacks of available approaches and
identifies promising avenues of research in this rapidly evolving
field. Even though there have been a number of different approaches
proposed in the literature for face recognition based on deep
learning methods, there is not a single book available in the
literature that gives a complete overview of these methods. The
proposed book captures the state of the art in face recognition
using various deep learning methods, and it covers a variety of
different topics related to face recognition. This book is aimed at
graduate students studying electrical engineering and/or computer
science. Biometrics is a course that is widely offered at both
undergraduate and graduate levels at many institutions around the
world: This book can be used as a textbook for teaching topics
related to face recognition. In addition, the work is beneficial to
practitioners in industry who are working on biometrics-related
problems. The prerequisites for optimal use are the basic knowledge
of pattern recognition, machine learning, probability theory, and
linear algebra.
This book provides an overview of different deep learning-based
methods for face recognition and related problems. Specifically,
the authors present methods based on autoencoders, restricted
Boltzmann machines, and deep convolutional neural networks for face
detection, localization, tracking, recognition, etc. The authors
also discuss merits and drawbacks of available approaches and
identifies promising avenues of research in this rapidly evolving
field. Even though there have been a number of different approaches
proposed in the literature for face recognition based on deep
learning methods, there is not a single book available in the
literature that gives a complete overview of these methods. The
proposed book captures the state of the art in face recognition
using various deep learning methods, and it covers a variety of
different topics related to face recognition. This book is aimed at
graduate students studying electrical engineering and/or computer
science. Biometrics is a course that is widely offered at both
undergraduate and graduate levels at many institutions around the
world: This book can be used as a textbook for teaching topics
related to face recognition. In addition, the work is beneficial to
practitioners in industry who are working on biometrics-related
problems. The prerequisites for optimal use are the basic knowledge
of pattern recognition, machine learning, probability theory, and
linear algebra.
Domain adaptation is an active, emerging research area that
attempts to address the changes in data distribution across
training and testing datasets. With the availability of a multitude
of image acquisition sensors, variations due to illumination and
viewpoint among others, computer vision applications present a very
natural test bed for evaluating domain adaptation methods. This
monograph provides a comprehensive overview of domain adaptation
solutions for visual recognition problems. By starting with the
problem description and illustrations, it discusses three
adaptation scenarios, namely, (i) unsupervised adaptation where the
""source domain"" training data is partially labeled and the
""target domain"" test data is unlabeled; (ii) semi-supervised
adaptation where the target domain also has partial labels; and
(iii) multi-domain heterogeneous adaptation which studies the
previous two settings with the source and/or target having more
than one domain, and accounts for cases where the features used to
represent the data in each domain are different. For all of these
scenarios, Domain Adaptation for Visual Recognition discusses the
existing adaptation techniques in the literature. These techniques
are motivated by the principles of max-margin discriminative
learning, manifold learning, sparse coding, as well as low-rank
representations, and have shown improved performance on a variety
of applications such as object recognition, face recognition,
activity analysis, concept classification, and person detection.
This book concludes by analyzing the challenges posed by the realm
of ""big visual data"" - in terms of the generalization ability of
adaptation algorithms to unconstrained data acquisition as well as
issues related to their computational tractability - and draws
parallels with efforts from the vision community on image
transformation models and invariant descriptors so as to facilitate
improved understanding of vision problems under uncertainty.
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