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Abstract
Object detection and classication algorithms are normally evaluated
on the basis of accuracy; how many misclassications does each produce
on a large dataset? The classication condence scores generated by al-
gorithms are somewhat arbitrary and dicult to compare or depend on.
This reduces trust by end users or later-stage decision making or rea-
soning algorithms which must decide which detections are likely to be
incorrect and allocate resources on that basis. Here we assess classier
reliability and uncertainty (entropy) in three scenarios: object detection
and categorisation in visual, synthetic aperture sonar and radar (SAS,
SAR) imagery. Techniques for obtaining probabilistic classications from
score-based decision algorithms such as support vector machines (SVMs)
are compared to classiers which produce probabilistic results as stan-
dard (Gaussian Process Classiers). Adaboost-based classiers are shown
to be both accurate and reliable for the vision modality. In the SAS and
SAR cases where these methods perform poorly, SVM-based classiers
outperform other options including GPCs.
on the basis of accuracy; how many misclassications does each produce
on a large dataset? The classication condence scores generated by al-
gorithms are somewhat arbitrary and dicult to compare or depend on.
This reduces trust by end users or later-stage decision making or rea-
soning algorithms which must decide which detections are likely to be
incorrect and allocate resources on that basis. Here we assess classier
reliability and uncertainty (entropy) in three scenarios: object detection
and categorisation in visual, synthetic aperture sonar and radar (SAS,
SAR) imagery. Techniques for obtaining probabilistic classications from
score-based decision algorithms such as support vector machines (SVMs)
are compared to classiers which produce probabilistic results as stan-
dard (Gaussian Process Classiers). Adaboost-based classiers are shown
to be both accurate and reliable for the vision modality. In the SAS and
SAR cases where these methods perform poorly, SVM-based classiers
outperform other options including GPCs.
Original language | English |
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Number of pages | 29 |
Journal | Image and vision computing |
Publication status | Unpublished - 10 Jun 2017 |
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Dive into the research topics of 'Improving Object Detector Algorithms using Uncertainty and Reliability'. Together they form a unique fingerprint.Projects
- 1 Finished
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Signal Processing in the Networked Battlespace
Mulgrew, B., Davies, M., Hopgood, J. & Thompson, J.
1/04/13 → 30/06/18
Project: Research