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Information Technology Journal
Year: 2011  |  Volume: 10  |  Issue: 5  |  Page No.: 944 - 954

A Hybrid Memetic Algorithm with Back-propagation Classifier for Fish Classification Based on Robust Features Extraction from PLGF and Shape Measurements

M. Alsmadi, K.B. Omar, S.A. Noah and I. Almarashdeh    

Abstract: The aim of this study is a novel hybrid approach for optimizing the performance of back-propagation classifier (BPC) by utilizing the ability of Memetic algorithm (genetic algorithm and great deluge algorithm) to optimize the parameters (weight) of the PBC for fish classification problem. To recognize an isolated pattern of fish in the image based on the combination between robust features extraction which extracted based on Potential Local Geometric Features (PLGF) and shape measurements, which are extracted by measuring the edge detection method, distance and angle measurements. Typical the BPC has such disadvantage as slow practice speed and easy for running into local minimum. We presented a system prototype for dealing with such problem. The process started by acquiring an image-containing pattern of fish, then the image features extraction is performed relying on PLGF and shape measurements. The hybrid Memetic Algorithm (genetic algorithm and great deluge algorithm) with BPC (HGAGD-BPC) has outperformed BPC method and previous methodologies by obtaining better quality results but with a high cost of computational time compared to the BPC method. Where the overall accuracy obtained using the traditional BPC was 86%, while the overall accuracy obtained by the HGAGD-BPC was 96% on the test dataset used. We developed a classifier for fish images classification. Eventually, the classifier is able to classify the given fish into poison and non-poison fish and classify the poison and non-poison fish into its family.

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