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Automatic Nile Tilapia Fish Classification Approachusing Machine Learning Techniques

Mohamed Mostafa Fouad

Arab Academy for Science, Technology, and Maritime TransportMember of SRGE Research Group.

Scientific Research Group in Egypt (SRGE)

Marine Ecological System

Understanding what is happening inside Marine Ecological System is vital to sustain the food system chain. Marine ecology describes the interactions of marine species with their biotic (living) and abiotic (nonliving) environments.

Ecological Footprint

Marine Ecological System

There is a need for underwater monitoring techniques to allows researchers to continuously track the changes in the marine ecological parameters, alerting them to the problems before they affect the marine livings.

What are the benefits???

Marine scientists always seek to monitor the populations of marine organisms, especially fish.

The importance of fish monitoring not only to understand fish, but also to understand:

1. Population dynamics, 2. Feeding habits,3. Species Classification,4. Knowing factors those affects fish species richness, 5. Also to implement management strategies such as setting

catch limits or providing species protection.6. etc.

Traditional Ways

Tagging && Tracking

Electrofishing

Divers

Echo Sounding Equipment

Lift nets

A question ?

Are there any other CHEAPER and EFFECTIVE ways???

Today’s breakthrough

New Video Capturing Tech.

Video Analysis Prog.

New High Capacity Storage.

Automatic Tilapia Fish Classification Approach

• The proposed approach investigates a number of image processing

techniques as a first step for supporting marine biologists to

distinguish the Nile Tilapia (Oreochromis niloticus) from other fish

populations of the Nile River.

Automatic Tilapia Fish Classification Approach, cont.

Automatic Tilapia Fish Classification Approach, cont.

• Applying Scale Invariant Feature Transform (SIFT)

• A well-known feature extraction algorithms

• The process of building a single SIFT keypoint descriptor:

(a) A Single SIFT keypoint selected from a Tilapia image,

(b) 16x16 pixel gradients,

(c) 4 x 4 cells keypoint descriptor with 8 pixel orientations

each.

The default length of a single SIFT keypoint descriptor is 4 x 4 x 8 =

128 element.

Automatic Tilapia Fish Classification Approach, cont.

• Applying Speeded Up Robust Features (SURF)

• A well-known feature extraction algorithms

• The SURF complexity is lower than SIFT since its algorithm fixes the

repetitive orientation using information from a circular region around the

interest point.

• Then, Following on, the algorithm constructs a square region aligned to

the selected orientation, and extract the descriptor (features) from it.

Automatic Tilapia Fish Classification Approach, cont.

• Phase one: Feature Extraction PhaseFeature

detector

Feature

descriptor

Set of Interesting Features

(Descriptors)

Automatic Tilapia Fish Classification Approach, cont.

In feature extraction phase different transformation should be considered

Different Affine Transformation

Different Scale Transformation

Different illumination

Automatic Tilapia Fish Classification Approach, cont.

• Phase two: Classification Phase

• Machine Learning (ML) classifiers use a set of features to characterize

each object within the input data based. The classification methods

used here in this article are considered as supervised learning methods.

SVM• Linear kernel function• Polynomial kernel function• RBF

K-NN

ANN

Classification Decision

Automatic Tilapia Fish Classification Approach, cont.

Features dataset

Classifier

Tilapia Class

Non-Tilapia Class

Experimental results

• The proposed classification approach was evaluated using 96 images of

Tilapia fish and 55 images of non-Tilapia fish.

• The evaluation criteria used are the recall and precision statistical equations

in addition to the Receiver Operating Characteristic (ROC) curves.

Experimental results, cont.

Results of Tilapia & Non-Tilapia Accuracy, Precision, and Recall for different feature extraction and classifiers.

Experimental results, cont.

• As shown in the previous table the implementation of the SURF feature

extraction algorithm combined with different SVM classifier kernel functions

(linear, or radial basis function (rbf), and polynomial), obtained experimental

results which showed the best precision with the rbf kernel function.

• Also, the SIFT gives a good results with the same SVM classifier.

• On the other hand, the ANN classifier has the least detectability rate

especially with the SIFT feature extraction algorithm that its detection of

Tilapia fish accuracy is 56.5%.

Experimental results, ROC, cont.

SURF feature extraction with SVM classifier using linear kernel function

SIFT feature extraction with SVM classifier using linear kernel function

Experimental results, ROC, cont.

SIFT feature extraction with ANN classifier

Experimental results, ROC, cont.

SURF feature extraction with SVM classifier using polynomial kernel function

SURF feature extraction with SVM classifier using rbf kernel function

Conclusions and Future work

• This research paper describes an approach for automating fish

classification. For each given input image, there is one of two possible

classes as an output. The out classes are Tilapia & Non-Tilapia classes.

• Not only the classification approach classifies the fish species, but also it

evaluates the performance of a number of machine learning algorithms

• such as SVM, KNN, ANN, when they combined with a well-known feature

extractions algorithms (SIFT, SURF).

• Experimental results obtained showed that the SVM classifier with a linear

function achieved the best performance with accuracy of 94.4% for the SURF

feature extraction.

Conclusions and Future work, cont.

• As well, the performance evaluation shows that the unfavorable classifier is

the ANN.

• Nevertheless applying radial basis kernel function with the SVM with the

SURF the approach achieved its maximum detectability rate.

• So, It can be concluded that the SVM using radial basis function with the

SURF feature extraction algorithm offers greater detectability of the Tilapia

species than the SIFT feature extraction algorithm.

• For future research, it is intended that the proposed method will serve as a

corner stone for research into counting and tracking fish species or any other

living organisms.

Thank youThank youE-mail: Mohamed_mostafa@aast.edu