3,783 research outputs found
One-Class Classification: Taxonomy of Study and Review of Techniques
One-class classification (OCC) algorithms aim to build classification models
when the negative class is either absent, poorly sampled or not well defined.
This unique situation constrains the learning of efficient classifiers by
defining class boundary just with the knowledge of positive class. The OCC
problem has been considered and applied under many research themes, such as
outlier/novelty detection and concept learning. In this paper we present a
unified view of the general problem of OCC by presenting a taxonomy of study
for OCC problems, which is based on the availability of training data,
algorithms used and the application domains applied. We further delve into each
of the categories of the proposed taxonomy and present a comprehensive
literature review of the OCC algorithms, techniques and methodologies with a
focus on their significance, limitations and applications. We conclude our
paper by discussing some open research problems in the field of OCC and present
our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure
Automatic human face detection for content-based image annotation
In this paper, an automatic human face detection approach using colour analysis is applied for content-based image annotation. In the face detection, the probable face region is detected by adaptive boosting algorithm, and then combined with a colour filtering classifier to enhance the accuracy in face detection. The initial experimental benchmark shows the proposed scheme can be efficiently applied for image annotation with higher fidelity
Micro-Doppler Based Human-Robot Classification Using Ensemble and Deep Learning Approaches
Radar sensors can be used for analyzing the induced frequency shifts due to
micro-motions in both range and velocity dimensions identified as micro-Doppler
(-D) and micro-Range (-R), respectively.
Different moving targets will have unique -D and
-R signatures that can be used for target classification.
Such classification can be used in numerous fields, such as gait recognition,
safety and surveillance. In this paper, a 25 GHz FMCW Single-Input
Single-Output (SISO) radar is used in industrial safety for real-time
human-robot identification. Due to the real-time constraint, joint
Range-Doppler (R-D) maps are directly analyzed for our classification problem.
Furthermore, a comparison between the conventional classical learning
approaches with handcrafted extracted features, ensemble classifiers and deep
learning approaches is presented. For ensemble classifiers, restructured range
and velocity profiles are passed directly to ensemble trees, such as gradient
boosting and random forest without feature extraction. Finally, a Deep
Convolutional Neural Network (DCNN) is used and raw R-D images are directly fed
into the constructed network. DCNN shows a superior performance of 99\%
accuracy in identifying humans from robots on a single R-D map.Comment: 6 pages, accepted in IEEE Radar Conference 201
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