Fish recognition based on robust features extraction from size and shape measurements using back-propagation classifier

Mutasem Khalil Alsmadi, Khairuddin Omar, Shahrul Azman Mohd Noah

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Image recognition is a challenging problem researchers had been research into this area for so long especially in the recent years, due to distortion, noise, segmentation errors, overlap and occlusion of objects in digital images. In our study, there are many fields concern with pattern recognition, for example, fingerprint verification, face recognition, iris discrimination, chromosome shape discrimination, optical character recognition, texture discrimination and speech recognition, the subject of pattern recognition appears. A system for recognizing isolated pattern of interest may be as an approach for dealing with such application. Scientists and engineers with interests in image processing and pattern recognition have developed various approaches to deal with digital image recognition problems such as, neural network, contour matching and statistics. In this study, our aim is to recognize an isolated pattern of interest in the image based on the combination between robust features extraction. Where depend on size and shape measurements, that are extracted by measuring the distance and geometrical measurements. We presented a system prototype for dealing with such problem. The system started by acquiring an image containing pattern of fish, then the image features extraction is performed relying on size and shape measurements. Our system has been applied on 20 different fish families, each family has a different number of fish types and our sample consists of distinct 610 of fish images. These images are divided into two datasets: 500 training images and 110 testing images. An overall accuracy is obtained using the back-propagation classifier was 86% on the test dataset used. We developed a classifier for fish images recognition. We efficiently have chosen a features extraction method to fit our demands. Our classifier successfully design and implement a decision which performed efficiently without any problems. Eventually, the classifier is able to categorize the given fish into its cluster and categorize the clustered fish into its poison or non-poison fish and categorizes the poison and non-poison fish into its family.

Original languageEnglish
Pages (from-to)489-494
Number of pages6
JournalInternational Review on Computers and Software
Volume5
Issue number4
Publication statusPublished - Jul 2010

Fingerprint

Backpropagation
Fish
Feature extraction
Classifiers
Image recognition
Pattern recognition
Optical character recognition
Chromosomes
Face recognition
Speech recognition
Image processing
Textures
Statistics
Neural networks
Engineers
Testing

Keywords

  • Ann
  • Digital image recognition and feed forward back propagation classifier
  • Distance and geometrical measurements
  • Feature extraction
  • Neural network
  • Poison and non-poison fish

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

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abstract = "Image recognition is a challenging problem researchers had been research into this area for so long especially in the recent years, due to distortion, noise, segmentation errors, overlap and occlusion of objects in digital images. In our study, there are many fields concern with pattern recognition, for example, fingerprint verification, face recognition, iris discrimination, chromosome shape discrimination, optical character recognition, texture discrimination and speech recognition, the subject of pattern recognition appears. A system for recognizing isolated pattern of interest may be as an approach for dealing with such application. Scientists and engineers with interests in image processing and pattern recognition have developed various approaches to deal with digital image recognition problems such as, neural network, contour matching and statistics. In this study, our aim is to recognize an isolated pattern of interest in the image based on the combination between robust features extraction. Where depend on size and shape measurements, that are extracted by measuring the distance and geometrical measurements. We presented a system prototype for dealing with such problem. The system started by acquiring an image containing pattern of fish, then the image features extraction is performed relying on size and shape measurements. Our system has been applied on 20 different fish families, each family has a different number of fish types and our sample consists of distinct 610 of fish images. These images are divided into two datasets: 500 training images and 110 testing images. An overall accuracy is obtained using the back-propagation classifier was 86{\%} on the test dataset used. We developed a classifier for fish images recognition. We efficiently have chosen a features extraction method to fit our demands. Our classifier successfully design and implement a decision which performed efficiently without any problems. Eventually, the classifier is able to categorize the given fish into its cluster and categorize the clustered fish into its poison or non-poison fish and categorizes the poison and non-poison fish into its family.",
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