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Problem statement: Image recognition was a challenging problem researchers had been&#13; research into this area for so long especially in the recent years, due to distortion, noise, segmentation&#13; errors, overlap and occlusion of objects in digital images. In our study, there are many fields concern&#13; with pattern recognition, for example, fingerprint verification, face recognition, iris discrimination,&#13; chromosome shape discrimination, optical character recognition, texture discrimination and speech&#13; recognition, the subject of pattern recognition appears. A system for recognizing isolated pattern of&#13; interest may be as an approach for dealing with such application. Scientists and engineers with&#13; interests in image processing and pattern recognition have developed various approaches to deal with&#13; digital image recognition problems such as, neural network, contour matching and statistics.&#13; Approach: In this study, our aim was to recognize an isolated pattern of interest (fish) in the image&#13; based robust features extraction. Where depend on color signatures that are extracted by RGB color&#13; space, color histogram and gray level co-occurrence matrix. Results: We presented a system prototype&#13; for dealing with such problem. The system started by acquiring an image containing pattern of fish,&#13; then the image segmentation was performed relying on color signature. Our system has been applied&#13; on 20 different fish families, each family has a different number of fish types and our sample consists&#13; of distinct 610 of fish images. These images are divided into two datasets: 400 training images and 210&#13; testing images. An overall accuracy was obtained using back-propagation classifier was 84% on the&#13; test dataset used. Conclusion: We developed a classifier for fish images recognition. We efficiently&#13; have chosen an image segmentation method to fit our demands. Our classifier successfully design and&#13; implement a decision which performed efficiently without any problems. Eventually, the classifier was&#13; able to categorize the given fish into its cluster and categorize the clustered fish into its poison or nonpoison&#13; fish and categorizes the poison and non-poison fish into its family

Fish Classification Based on Robust Features Extraction From Color Signature Using Back-Propagation Classifier