期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2014
卷号：66
期号：3
出版社：Journal of Theoretical and Applied
摘要：Problem statement: One challenging research area nowadays is pattern recognition. Many applications lay under the field of pattern recognition such as face and iris recognition, speech recognition, texture discrimination and optical character recognition. A system that recognizes isolated pattern of interest is called pattern recognition. The pattern under consideration could be an image. During the process of image recognition, many problems could occur such as noise, distortion, overlap, errors in the segmentation results and obstruction of objects in the image. Several approaches to handle and solve pattern recognition problems have been developed. Examples of such approaches are neural networks (NN), Contour matching, texture and color signature. Approach: The aim of this study is to develop a system to recognize isolated fish object in the image based on a combination between significant extracted features using anchor points, texture and statistical measurements. A generic fish classification could then be performed using a hybrid meta-heuristic algorithms (genetic algorithm with iterated local search) with back-propagation algorithm (GAILS-BPC), to classify the images of fish into dangerous and non-dangerous families, and to recognize the dangerous fish families into Predatory and Poison fish family, and recognize the non-dangerous fish families into garden and food fish family. Conclusion and Results: A prototype to deal with the problem of fish images classification is presented in this research work. The proposed prototype has been tested based on 24 fish families, each family contains different number of species. The proposed prototype has performed the classification process successfully. The experimental tests have been performed based on 320 distinct fish images. The 320 distinct fish images were divided into 220 images for training phase and 100 images for testing phase. An overall accuracy recognition rate is 80.5% that was obtained using the proposed GAILS-BPC.
关键词：Feature Extraction; Anchor Measurements; Gabor filter; statistical measurements; Fish Images; Back Propagation Classifier; Iterated Local Search and Genetic Algorithm.
