Novel Consensus Approaches to the Reliable Ranking of Features for Seabed Imagery Classification

Harrison, R.F.; Birchall, Roger; Mann, Dave; Wang, Wenjia

doi:10.1142/s0129065712500268

Cited by 7 publications

(3 citation statements)

References 35 publications

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“…The crucial aspect about using RF as a basis for feature selection is that feature interactions are taken into account. Another, perhaps preferable option would be to apply a feature selection wrapper for each specific classifier [28], [44]. This would involve using each classifier as a ‘black box’ and assess performance on subsets of features.…”

Section: Discussionmentioning

confidence: 99%

A Comparison of Supervised Classification Methods for the Prediction of Substrate Type Using Multibeam Acoustic and Legacy Grain-Size Data

2014

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Detailed seabed substrate maps are increasingly in demand for effective planning and management of marine ecosystems and resources. It has become common to use remotely sensed multibeam echosounder data in the form of bathymetry and acoustic backscatter in conjunction with ground-truth sampling data to inform the mapping of seabed substrates. Whilst, until recently, such data sets have typically been classified by expert interpretation, it is now obvious that more objective, faster and repeatable methods of seabed classification are required. This study compares the performances of a range of supervised classification techniques for predicting substrate type from multibeam echosounder data. The study area is located in the North Sea, off the north-east coast of England. A total of 258 ground-truth samples were classified into four substrate classes. Multibeam bathymetry and backscatter data, and a range of secondary features derived from these datasets were used in this study. Six supervised classification techniques were tested: Classification Trees, Support Vector Machines, k-Nearest Neighbour, Neural Networks, Random Forest and Naive Bayes. Each classifier was trained multiple times using different input features, including i) the two primary features of bathymetry and backscatter, ii) a subset of the features chosen by a feature selection process and iii) all of the input features. The predictive performances of the models were validated using a separate test set of ground-truth samples. The statistical significance of model performances relative to a simple baseline model (Nearest Neighbour predictions on bathymetry and backscatter) were tested to assess the benefits of using more sophisticated approaches. The best performing models were tree based methods and Naive Bayes which achieved accuracies of around 0.8 and kappa coefficients of up to 0.5 on the test set. The models that used all input features didn't generally perform well, highlighting the need for some means of feature selection.

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Section: Discussionmentioning

confidence: 99%

A Comparison of Supervised Classification Methods for the Prediction of Substrate Type Using Multibeam Acoustic and Legacy Grain-Size Data

2014

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“…Researchers have proposed a number of methods for decoding EEG signals. [20][21][22][23][24][25] These methods include preprocessing techniques for improving signal-to-noise ratio (SNR), feature extraction for capturing essential information, and classification methods for giving a judgment. Judgment from EEG decoding is then translated into commands to control an external device.…”

Section: Introductionmentioning

confidence: 99%

Design of Assistive Wheelchair System Directly Steered by Human Thoughts

Liang

Zhao

et al. 2013

Int. J. Neur. Syst.

105

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Integration of brain-computer interface (BCI) technique and assistive device is one of chief and promising applications of BCI system. With BCI technique, people with disabilities do not have to communicate with external environment through traditional and natural pathways like peripheral nerves and muscles, and could achieve it only by their brain activities. In this paper, we designed an electroencephalogram (EEG)-based wheelchair which can be steered by users' own thoughts without any other involvements. We evaluated the feasibility of BCI-based wheelchair in terms of accuracies and real-world testing. The results demonstrate that our BCI wheelchair is of good performance not only in accuracy, but also in practical running testing in a real environment. This fact implies that people can steer wheelchair only by their thoughts, and may have a potential perspective in daily application for disabled people.

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“…The applications are plenty and include freeway work zone analysis [1,27,36,37], automatic image search [34], human detection and modeling [12,69] and face recognition [7]. Object recognition in images has become a very important topic in the fields of traffic infrastructure and driving assistance system [30,54]. Applications such as traffic signs recognition [32,53,56,65], obstacle avoidance [19] and traffic surveillance [59] have gotten the attention of the industry for some time now.…”

Section: Introductionmentioning

confidence: 99%

Pedestrian detection in far infrared images

Olmeda

Premebida

Nunes

et al. 2013

ICA

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This paper presents an experimental study on pedestrian classification and detection in far infrared (FIR) images. The study includes an in-depth evaluation of several combinations of features and classifiers, which include features previously used for daylight scenarios, as well as a new descriptor (HOPE -Histograms of Oriented Phase Energy), specifically targeted to infrared images, and a new adaptation of a latent variable SVM approach to FIR images. The presented results are validated on a new classification and detection dataset of FIR images collected in outdoor environments from a moving vehicle. The classification space contains 16152 pedestrians and 65440 background samples evenly selected from several sequences acquired at different temperatures and different illumination conditions. The detection dataset consist on 15224 images with ground truth information. The authors are making this dataset public for benchmarking new detectors in the area of intelligent vehicles and field robotics applications.

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Novel Consensus Approaches to the Reliable Ranking of Features for Seabed Imagery Classification

Cited by 7 publications

References 35 publications

A Comparison of Supervised Classification Methods for the Prediction of Substrate Type Using Multibeam Acoustic and Legacy Grain-Size Data

A Comparison of Supervised Classification Methods for the Prediction of Substrate Type Using Multibeam Acoustic and Legacy Grain-Size Data

Design of Assistive Wheelchair System Directly Steered by Human Thoughts

Pedestrian detection in far infrared images

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