Fish Recognition Based on Robust Features Extraction from Color Texture Measurements Using Back-propagation Classifier

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.