230 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
An ensemble of rejecting classifiers for anomaly detection of audio events
Audio analytic systems are receiving an increasing interest in the scientific community, not only as stand alone
systems for the automatic detection of abnormal events by
the interpretation of the audio track, but also in conjunction with video analytics tools for enforcing the evidence of
anomaly detection. In this paper we present an automatic
recognizer of a set of abnormal audio events that works by
extracting suitable features from the signals obtained by microphones installed into a surveilled area, and by classifying them using two classifiers that operate at different time
resolutions. An original aspect of the proposed system is the
estimation of the reliability of each response of the individual classifiers. In this way, each classifier is able to reject
the samples having an overall reliability below a threshold. This approach allows our system to combine only reliable decisions, so increasing the overall performance of
the method. The system has been tested on a large dataset
of samples acquired from real world scenarios; the audio
classes of interests are represented by gunshot, scream and
glass breaking in addition to the background sounds. The
preliminary results obtained encourage further research in
this direction
Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning
Learning-based pattern classifiers, including deep networks, have shown
impressive performance in several application domains, ranging from computer
vision to cybersecurity. However, it has also been shown that adversarial input
perturbations carefully crafted either at training or at test time can easily
subvert their predictions. The vulnerability of machine learning to such wild
patterns (also referred to as adversarial examples), along with the design of
suitable countermeasures, have been investigated in the research field of
adversarial machine learning. In this work, we provide a thorough overview of
the evolution of this research area over the last ten years and beyond,
starting from pioneering, earlier work on the security of non-deep learning
algorithms up to more recent work aimed to understand the security properties
of deep learning algorithms, in the context of computer vision and
cybersecurity tasks. We report interesting connections between these
apparently-different lines of work, highlighting common misconceptions related
to the security evaluation of machine-learning algorithms. We review the main
threat models and attacks defined to this end, and discuss the main limitations
of current work, along with the corresponding future challenges towards the
design of more secure learning algorithms.Comment: Accepted for publication on Pattern Recognition, 201
Unsupervised Anomaly Detection with Rejection
Anomaly detection aims at detecting unexpected behaviours in the data.
Because anomaly detection is usually an unsupervised task, traditional anomaly
detectors learn a decision boundary by employing heuristics based on
intuitions, which are hard to verify in practice. This introduces some
uncertainty, especially close to the decision boundary, that may reduce the
user trust in the detector's predictions. A way to combat this is by allowing
the detector to reject examples with high uncertainty (Learning to Reject).
This requires employing a confidence metric that captures the distance to the
decision boundary and setting a rejection threshold to reject low-confidence
predictions. However, selecting a proper metric and setting the rejection
threshold without labels are challenging tasks. In this paper, we solve these
challenges by setting a constant rejection threshold on the stability metric
computed by ExCeeD. Our insight relies on a theoretical analysis of such a
metric. Moreover, setting a constant threshold results in strong guarantees: we
estimate the test rejection rate, and derive a theoretical upper bound for both
the rejection rate and the expected prediction cost. Experimentally, we show
that our method outperforms some metric-based methods
Audio-visual multi-modality driven hybrid feature learning model for crowd analysis and classification
The high pace emergence in advanced software systems, low-cost hardware and decentralized cloud computing technologies have broadened the horizon for vision-based surveillance, monitoring and control. However, complex and inferior feature learning over visual artefacts or video streams, especially under extreme conditions confine majority of the at-hand vision-based crowd analysis and classification systems. Retrieving event-sensitive or crowd-type sensitive spatio-temporal features for the different crowd types under extreme conditions is a highly complex task. Consequently, it results in lower accuracy and hence low reliability that confines existing methods for real-time crowd analysis. Despite numerous efforts in vision-based approaches, the lack of acoustic cues often creates ambiguity in crowd classification. On the other hand, the strategic amalgamation of audio-visual features can enable accurate and reliable crowd analysis and classification. Considering it as motivation, in this research a novel audio-visual multi-modality driven hybrid feature learning model is developed for crowd analysis and classification. In this work, a hybrid feature extraction model was applied to extract deep spatio-temporal features by using Gray-Level Co-occurrence Metrics (GLCM) and AlexNet transferrable learning model. Once extracting the different GLCM features and AlexNet deep features, horizontal concatenation was done to fuse the different feature sets. Similarly, for acoustic feature extraction, the audio samples (from the input video) were processed for static (fixed size) sampling, pre-emphasis, block framing and Hann windowing, followed by acoustic feature extraction like GTCC, GTCC-Delta, GTCC-Delta-Delta, MFCC, Spectral Entropy, Spectral Flux, Spectral Slope and Harmonics to Noise Ratio (HNR). Finally, the extracted audio-visual features were fused to yield a composite multi-modal feature set, which is processed for classification using the random forest ensemble classifier. The multi-class classification yields a crowd-classification accurac12529y of (98.26%), precision (98.89%), sensitivity (94.82%), specificity (95.57%), and F-Measure of 98.84%. The robustness of the proposed multi-modality-based crowd analysis model confirms its suitability towards real-world crowd detection and classification tasks
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