Classification of fish images based on shape characteristic
This research work has been conducted to analyze and classify the types of fish image based on shape characteristic. The features of characteristic of fish image are extracted by using three Moment Invariants (MI) techniques and Fourier descriptors (FD). The types of Moment invariants are Geometr...
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| Format: | Thesis |
| Language: | English |
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| Online Access: | http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/61988/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/61988/2/Full%20text.pdf |
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| Summary: | This research work has been conducted to analyze and classify the types of fish image
based on shape characteristic. The features of characteristic of fish image are extracted
by using three Moment Invariants (MI) techniques and Fourier descriptors (FD). The
types of Moment invariants are Geometric moment invariant (GMI), United moment
invariant (UMI), Zernike moment invariant (ZMI). In the FD’s technique, there are two
edge detection have been used to create the boundary of the image, namely Robert cross
detection and Sobel cross detection. These feature extraction techniques have been used
to analyze the image due to its invariant features of an image based on translation,
scaling factor and rotation. There are two ways to examine the performance of feature
extraction techniques, namely intra-class analysis and classification analysis. For the
intra-class analysis, a set of equations has been implemented to find the best technique
among the three different types of moments and Fourier descriptors based on the low
value of Total Percentage Min Absolute Error (TPMAE). Meanwhile, for the
classification analysis, the Artificial Neural Network (ANN) is explored and adapted to
classify the fish images. The feature vectors produce by feature extraction techniques
that represent the image are used as the input of classification. The results of the intraclass
analysis indicate that the UMI was the best technique among the moment
techniques while Fourier descriptor by using the Sobel edge detection shows the lower
TPMAE as compared to Robert edge detection. For the classification part, two types of
ANN’s which are Multilayer Perceptron (MLP) and Simplified Fuzzy ARTMAP
(SFAM) neural networks have been used to classify the image based on fish category.
The Leverberg-Marquardt (LM) algorithm is used to train the MLP network in order to
check the applicability. Based on the classification that has been computed, the results
show that all networks perform good classification performance with overall accuracy is
around 90%. However, the MLP trained by Leverberg-Marquardt shows the highest
classification performance in classifying the fish images as compared to the SFAM
network. |
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