Image processing features extraction on fish behaviour

Mohd Azraai Mohd Razman*, Anwar P. P. Abdul Majeed, Rabiu Muazu Musa, Zahari Taha, Gian Antonio Susto, Yukinori Mukai

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingChapterpeer-review


This chapter demonstrates the pipeline from data collection until classifier models that achieve the best possible model in identifying the disparity between hunger states. The pre-processing segment describes the features of the data sets obtained by means of image processing. The method includes the simple moving average (SMA), downsizing factors, dynamic time warping (DTW) and clustering by the k-means method. This is to rationally assign the necessary significant information from the data collected and processed the images captured for demand feeder and fish motion as a synthesis for anticipating the state of fish starvation. The selection of features in this study takes place via the boxplot analysis and the principal component analysis (PCA) on dimensionality reduction. Finally, the validation of the hunger state will be addressed by comparing machine learning (ML) classifiers, namely the discriminant analysis (DA), support vector machine (SVM) and k-nearest neighbour (k-NN). The outcome in this chapter will validate the features from image processing as a tool for identifying the behavioural changes of the fish in school size.

Original languageEnglish
Title of host publicationSpringerBriefs in Applied Sciences and Technology
Number of pages12
Publication statusPublished - 2020
Externally publishedYes

Publication series

NameSpringerBriefs in Applied Sciences and Technology
ISSN (Print)2191-530X
ISSN (Electronic)2191-5318


  • Boxplot analysis
  • Classification
  • Dynamic time warping
  • Features selection
  • K-means clustering
  • PCA


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