Abstract
This research is to present a new approach for the classification of the Penaeid Prawn Species. The extraction of Texture features based on the Gabor filter is proposed in this method. These extracted features are used for the classification of Penaeid Prawn Species based on Radial Basis Probabilistic Neural Networks and Support Vector Machines. The texture of the prawn image are extracted based on different scales and orientations by which mean and standard deviation are calculated. The resultant Gabor feature values are fed as input to Radial basis Probabilistic Neural Network Classifier for the classification of the species. The experimental results show the performance of the extracted feature vectors for Penaeid Prawn species recognition. The RBPNN gives better recognition when compared with Support vector machines.
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