19,610 research outputs found
Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective
This paper takes a problem-oriented perspective and presents a comprehensive
review of transfer learning methods, both shallow and deep, for cross-dataset
visual recognition. Specifically, it categorises the cross-dataset recognition
into seventeen problems based on a set of carefully chosen data and label
attributes. Such a problem-oriented taxonomy has allowed us to examine how
different transfer learning approaches tackle each problem and how well each
problem has been researched to date. The comprehensive problem-oriented review
of the advances in transfer learning with respect to the problem has not only
revealed the challenges in transfer learning for visual recognition, but also
the problems (e.g. eight of the seventeen problems) that have been scarcely
studied. This survey not only presents an up-to-date technical review for
researchers, but also a systematic approach and a reference for a machine
learning practitioner to categorise a real problem and to look up for a
possible solution accordingly
A Novel Hybrid CNN-AIS Visual Pattern Recognition Engine
Machine learning methods are used today for most recognition problems.
Convolutional Neural Networks (CNN) have time and again proved successful for
many image processing tasks primarily for their architecture. In this paper we
propose to apply CNN to small data sets like for example, personal albums or
other similar environs where the size of training dataset is a limitation,
within the framework of a proposed hybrid CNN-AIS model. We use Artificial
Immune System Principles to enhance small size of training data set. A layer of
Clonal Selection is added to the local filtering and max pooling of CNN
Architecture. The proposed Architecture is evaluated using the standard MNIST
dataset by limiting the data size and also with a small personal data sample
belonging to two different classes. Experimental results show that the proposed
hybrid CNN-AIS based recognition engine works well when the size of training
data is limited in siz
Eye in the Sky: Real-time Drone Surveillance System (DSS) for Violent Individuals Identification using ScatterNet Hybrid Deep Learning Network
Drone systems have been deployed by various law enforcement agencies to
monitor hostiles, spy on foreign drug cartels, conduct border control
operations, etc. This paper introduces a real-time drone surveillance system to
identify violent individuals in public areas. The system first uses the Feature
Pyramid Network to detect humans from aerial images. The image region with the
human is used by the proposed ScatterNet Hybrid Deep Learning (SHDL) network
for human pose estimation. The orientations between the limbs of the estimated
pose are next used to identify the violent individuals. The proposed deep
network can learn meaningful representations quickly using ScatterNet and
structural priors with relatively fewer labeled examples. The system detects
the violent individuals in real-time by processing the drone images in the
cloud. This research also introduces the aerial violent individual dataset used
for training the deep network which hopefully may encourage researchers
interested in using deep learning for aerial surveillance. The pose estimation
and violent individuals identification performance is compared with the
state-of-the-art techniques.Comment: To Appear in the Efficient Deep Learning for Computer Vision (ECV)
workshop at IEEE Computer Vision and Pattern Recognition (CVPR) 2018. Youtube
demo at this: https://www.youtube.com/watch?v=zYypJPJipY
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