Underwater Object Recognition: A Domain-Adaption Methodology of Machine Learning Classifiers

• Type: Article in International Conference Proceedings Book
• Date: 2019

Authors

Afonso, APO (Author) Other; Pinto, AM (Author) FEUP

Conference proceedings International

Title: OCEANS 2019 MTS/IEEE Seattle, OCEANS 2019

2019 OCEANS MTS/IEEE Seattle, OCEANS 2019

27 October 2019 through 31 October 2019

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 2 Citations

Other information

Authenticus ID: P-00R-PX8

DOI: 10.23919/oceans40490.2019.8962693

Abstract (EN): This paper presents a novel dataset, composed of images of objects in two distinct environments and both controlled and uncontrolled capture conditions, aimed at serving as a benchmark for domain-adaptation image classification algorithms in an air versus underwater context. All images are fully annotated, extending the use of the dataset for detection as well as segmentation tasks. An exemplifying use-case is tested, where the performance of a Support Vector Machine applied to a Bag-of-Visual-Words and SIFT features is evaluated on both domains, with different training methodologies. Results demonstrate that the conventional classifier used has a lack of generalization ability, with a poor transfer of knowledge from the aerial to the aquatic domain.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6