Abstract
Problem statement: 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. Approach: in this work, our aim is to recognize an isolated pattern of interest in the image based on the combination between robust features extraction. Where depend on color texture measurements that are extracted by gray level co-occurrence matrix. Result: We presented a system prototype for dealing with such problem. The system started by acquiring an image containing pattern of fish, then the image segmentation is performed relying on color texture 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 backpropagation classifier was 84% on the test dataset used. Conclusion: We developed a classifier for fish images recognition. We efficiently have chosen a image segmentation 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 language | English |
|---|---|
| Pages (from-to) | 11-18 |
| Number of pages | 8 |
| Journal | Journal of Theoretical and Applied Information Technology |
| Volume | 18 |
| Issue number | 1 |
| Publication status | Published - 2010 |
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Keywords
- ANN
- Color texture
- Digital image recognition
- Feed forward back propagation classifier
- Gray level co-occurrence matrix
- Image segmentation
- Neural network
- Poison and non-poison fish
ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
Fish recognition based on robust features extraction from color texture measurements using back-propagation classifier. / Alsmadi, Mutasem Khalil; Omar, Khairuddin; Mohd Noah, Shahrul Azman; Almarashdeh, Ibrahim.
In: Journal of Theoretical and Applied Information Technology, Vol. 18, No. 1, 2010, p. 11-18.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Fish recognition based on robust features extraction from color texture measurements using back-propagation classifier
AU - Alsmadi, Mutasem Khalil
AU - Omar, Khairuddin
AU - Mohd Noah, Shahrul Azman
AU - Almarashdeh, Ibrahim
PY - 2010
Y1 - 2010
N2 - Problem statement: 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. Approach: in this work, our aim is to recognize an isolated pattern of interest in the image based on the combination between robust features extraction. Where depend on color texture measurements that are extracted by gray level co-occurrence matrix. Result: We presented a system prototype for dealing with such problem. The system started by acquiring an image containing pattern of fish, then the image segmentation is performed relying on color texture 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 backpropagation classifier was 84% on the test dataset used. Conclusion: We developed a classifier for fish images recognition. We efficiently have chosen a image segmentation 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.
AB - Problem statement: 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. Approach: in this work, our aim is to recognize an isolated pattern of interest in the image based on the combination between robust features extraction. Where depend on color texture measurements that are extracted by gray level co-occurrence matrix. Result: We presented a system prototype for dealing with such problem. The system started by acquiring an image containing pattern of fish, then the image segmentation is performed relying on color texture 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 backpropagation classifier was 84% on the test dataset used. Conclusion: We developed a classifier for fish images recognition. We efficiently have chosen a image segmentation 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.
KW - ANN
KW - Color texture
KW - Digital image recognition
KW - Feed forward back propagation classifier
KW - Gray level co-occurrence matrix
KW - Image segmentation
KW - Neural network
KW - Poison and non-poison fish
UR - http://www.scopus.com/inward/record.url?scp=78650285613&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78650285613&partnerID=8YFLogxK
M3 - Article
VL - 18
SP - 11
EP - 18
JO - Journal of Theoretical and Applied Information Technology
JF - Journal of Theoretical and Applied Information Technology
SN - 1992-8645
IS - 1
ER -