Classification of corals in reflectance and fluorescence images using convolutional neural network representations

Lian Xu, Mohammed Bennamoun, Senjian An, Ferdous Sohe, Farid Boussaid

Research output: Chapter in Book/Conference paperConference paper

4 Citations (Scopus)

Abstract

Coral species, with complex morphology and ambiguous boundaries, pose a great challenge for automated classification. CNN activations, which are extracted from fully connected layers of deep networks (FC features), have been successfully used as powerful universal representations in many visual tasks. In this paper, we investigate the transferability and combined performance of FC features and CONY features (extracted from convolutional layers) in the coral classification of two image modalities (reflectance and fluorescence), using a typical deep network (e.g. VGGNet). We exploit vector of locally aggregated descriptors (VLAD) encoding and principal component analysis (PCA) to compress dense CONY features into a compact representation. Experimental results demonstrate that encoded CONV3 features achieve superior performances on reflectance and fluorescence coral images, compared to FC features. The combination of these two features further improves the overall accuracy and achieves state-of-the-art performance on the challenging EFC dataset.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1493-1497
Number of pages5
Volume2018-April
ISBN (Print)9781538646588
DOIs
Publication statusPublished - 10 Sep 2018
Event2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada
Duration: 15 Apr 201820 Apr 2018

Conference

Conference2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
Abbreviated titleICASSP 2018
CountryCanada
CityCalgary
Period15/04/1820/04/18

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  • Cite this

    Xu, L., Bennamoun, M., An, S., Sohe, F., & Boussaid, F. (2018). Classification of corals in reflectance and fluorescence images using convolutional neural network representations. In 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings (Vol. 2018-April, pp. 1493-1497). [8462574] IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICASSP.2018.8462574