Computer Science – Computer Vision and Pattern Recognition
Scientific paper
2009-12-05
International Journal of Computer Science and Information Security, IJCSIS, Vol. 6, No. 2, pp. 215-221, November 2009, USA
Computer Science
Computer Vision and Pattern Recognition
7 pages IEEE format, International Journal of Computer Science and Information Security, IJCSIS November 2009, ISSN 1947 5500,
Scientific paper
We presents in this paper a novel fish classification methodology based on a combination between robust feature selection, image segmentation and geometrical parameter techniques using Artificial Neural Network and Decision Tree. Unlike existing works for fish classification, which propose descriptors and do not analyze their individual impacts in the whole classification task and do not make the combination between the feature selection, image segmentation and geometrical parameter, we propose a general set of features extraction using robust feature selection, image segmentation and geometrical parameter and their correspondent weights that should be used as a priori information by the classifier. In this sense, instead of studying techniques for improving the classifiers structure itself, we consider it as a black box and focus our research in the determination of which input information must bring a robust fish discrimination.The main contribution of this paper is enhancement recognize and classify fishes based on digital image and To develop and implement a novel fish recognition prototype using global feature extraction, image segmentation and geometrical parameters, it have the ability to Categorize the given fish into its cluster and Categorize the clustered fish into poison or non-poison fish, and categorizes the non-poison fish into its family .
Almarashdah Ibrahim
Noah Shahrul Azman
Omar Khairuddin Bin
Sari Alsmadi Mutasem Khalil
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