A Comparative Study for Fisheye Image Classification: SVM or DNN

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Authors

The comparison between the feature-based method and the learning-based method is conducted in the training time, the accuracy and the generalization capacity, to address the optimisation for the multi-style fisheye imagery classification. We construct an srd-SIFT descriptor based SVM classifier to present the feature-based method for describing the influence of the dataset scale and the visual word scale on the classifier. The SVM classifier achieves 15.98% accuracy on the test set after 162 h training, with the condition that includes 800 images per class in 12 classes and 1500 visual words. For the learning-based method, we propose to expand training samples’ style variety, via style transformation, to facilitate the contemporary architecture retraining. Following this approach, we retrain the ResNet-50 by an artificial multi-style fisheye image dataset without complementing new training labels. The performance of the obtained ResNet classifier is evaluated on 6000 images collected in the real-world. The result shows that the retrained classifier has great generalization capacity and reaches 97.19% top-3 accuracy.

Original languageEnglish
Title of host publicationProceedings of the 12th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2020
EditorsAjith Abraham, Yukio Ohsawa, Niketa Gandhi, M. A. Jabbar, Abdelkrim Haqiq, Seán McLoone, Biju Issac
Number of pages10
PublisherSpringer Science and Business Media Deutschland GmbH
Publication date2021
Pages424-433
ISBN (Print)978-3-030-73688-0
ISBN (Electronic)978-3-030-73689-7
DOIs
Publication statusPublished - 2021
Event12th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2020 and 16th International Conference on Information Assurance and Security, IAS 2020 - Virtual, Online
Duration: 15.12.202018.12.2020
Conference number: 12 & 16

    Research areas

  • DNN, Fisheye image, Style expansion, Super vector machine
  • Engineering