Fish recognition based on the combination between robust feature selection, image segmentation and geometrical parameter techniques using Artificial Neural Network and Decision Tree

-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 study area selected for our proposed method from global information system (GIS) on Fishes (fish-base) and department of fisheries Malaysia ministry of agricultural and Agro-based industry in putrajaya, Malaysia region currently, the database contains 1513 of fish images. Data acquired on 22th August, 2008, is used. The classification problem involved the identification of 1513 types of image fishes; family ,Scientific Name , English name , local name, Habitat , poison fish and nonpoison .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 nonpoison fish into its family . Both classification and recognition are based on combination between robust feature selection, image segmentation and geometrical parameter techniques.