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Towards multi-class detection: a self-learning approach to reduce inter-class noise from training dataset
Author(s): Amir Ghahremani; Egor Bondarev; Peter H. N. de With
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Paper Abstract

This paper proposes a novel self-learning framework, which converts a noisy, pre-labeled multi-class object dataset into a purified multi-class object dataset with object bounding-box annotations, by iteratively removing noise samples from the low-quality dataset, which may contain a high level of inter-class noise samples. The framework iteratively purifies the noisy training datasets for each class and updates the classification model for multiple classes. The procedure starts with a generic single-class object model which changes to a multi-class model in an iterative procedure of which the F-1 score is evaluated to reach a sufficiently high score. The proposed framework is based on learning the used models with CNNs. As a result, we obtain a purified multi-class dataset and as a spin-off, the updated multi-class object model. The proposed framework is evaluated on maritime surveillance, where vessels need to be classified into eight different types. The experimental results on the evaluation dataset show that the proposed framework improves the F-1 score approximately by 5% and 25% at the end of the third iteration, while the initial training datasets contain 40% and 60% inter-class noise samples (erroneously classified labels of vessels and without annotations), respectively. Additionally, the recall rate increases nearly by 38% (for the more challenging 60% inter-class noise case), while the mean Average Precision (mAP) rate remains stable.

Paper Details

Date Published: 15 March 2019
PDF: 8 pages
Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 110411M (15 March 2019); doi: 10.1117/12.2523182
Show Author Affiliations
Amir Ghahremani, Eindhoven Univ. of Technology (Netherlands)
Egor Bondarev, Eindhoven Univ. of Technology (Netherlands)
Peter H. N. de With, Eindhoven Univ. of Technology (Germany)


Published in SPIE Proceedings Vol. 11041:
Eleventh International Conference on Machine Vision (ICMV 2018)
Antanas Verikas; Dmitry P. Nikolaev; Petia Radeva; Jianhong Zhou, Editor(s)

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