Title

Evaluation of different features and machine learning classifiers for classification of rays from underwater digital images

Document Type

Conference Proceeding

Publisher

IEEE

Place of Publication

Sydney, Australia

School

School of Science

RAS ID

28106

Comments

Originally published as: Moniruzzaman, M. D., & Islam, S. S. (2018, December). Evaluation of Different Features and Classifiers for Classification of Rays from Underwater Digital Images. In 2018 International Conference on Machine Learning and Data Engineering (iCMLDE) (pp. 83-90). IEEE. Original paper available here

Abstract

Machine learning technique based classification approaches have proved credibility and gained popularity in recent years. Different feature extraction methods from two dimensional images along with machine learning classifiers are being used for different computer vision problem solving approaches. To address the challenges of image classification, sourced from underwater imaging, we have experimented on some prominent feature extraction algorithms namely histogram of oriented gradients (HOG) , local binary patterns (LBP), bag-of-features (SURF), and neural network based feature extraction on a dataset containing total 305 underwater images of blue spotted and manta rays. As classifiers, we have experimented on decision tree, support vector machine (SVM), k-nearest neighbours (KNN), and neural network (two-layered NN). Results show that the combination of bag-of-features with twolayered neural network classifiers performed better than others for the selected underwater image dataset. As a tool, we used Matlab for this experimentation.

DOI

10.1109/iCMLDE.2018.00025

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