Fish species classification by color,texture and multi-class support vector machine using computer vision |
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| Institution: | 1. China-EU Center for Information and Communication Technologies in Agriculture, China Agriculture University, Beijing 100083, PR China;2. Beijing Engineering & Technology Research Center for Internet of Things in Agriculture, Beijing 100083, PR China;3. Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing 100083, PR China;4. Beijing Engineering Center for Advanced Sensors in Agriculture, Beijing 100083, PR China;5. College of Information Technology, Jilin Agricultural University, Changchun 130118, PR China;1. Biology Department, Campus University of Voutes, Heraklion, Crete 70013, Greece;2. Foundation for Research and Technology, Institute of Electronic Structure and Laser, Heraklion, Crete 71110, Greece;1. PSL, Labex CORAIL, USR 3278 CNRS-EPHE-UPVD, Centre de Recherche Insulaire et Observatoire de l’Environnement (CRIOBE), BP 1013, Papetoai, 98729 Moorea, French Polynesia;2. Hawai''i Institute of Marine Biology, University of Hawai''i at Mānoa, P. O. Box 1346, Kaneohe, Hawai''i, United States;1. Institute of Control Systems and Industrial Computing (AI2), Universitat Politècnica de València (UPV), Camí de Vera (s/n), 46022 València, Spain;2. Institut d’Investigació per a la Gestió Integrada de Zones Costaneres (IGIC), Universitat Politècnica de València (UPV), Camí de Vera (s/n), 46022 València, Spain;1. Department of Electronics and Communication Engineering, Amity University, Noida, Uttar Pradesh, India;2. ICAR - Indian Institute of Agricultural Biotechnology, Namkum, Ranchi, Jharkhand 834010, India;1. Institute of Control Systems and Industrial Computing (AI2), Universitat Politècnica de València (UPV), Camino de Vera (s/n), 46022 Valencia, Spain;2. Institut d’Investigació per a la Gestió Integrada de Zones Costaneres (IGIC), Universitat Politècnica de València (UPV), Camino de Vera (s/n), 46022 Valencia, Spain |
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| Abstract: | Remote diagnose of fish diseases for farmers is unrealized in China, but use of mobile phones and remote analysis based on image processing can be feasible due to the widespread use of mobile phones with camera features in rural areas. This paper presents a novel method of classifying species of fish based on color and texture features and using a multi-class support vector machine (MSVM). Fish images were acquired and sent by smartphone, and the method utilized was comprised of the following stages. Color and texture subimages of fish skin were obtained from original images. Color features, statistical texture features and wavelet-based texture features of the color and texture subimages were extracted, and six groups of feature vectors were composed. LIBSVM software was tested using leave-one-out cross validation to find the best group for classification in feature selection procedure. Two multi-class support vector machines based on a one-against-one algorithm were constructed for classification. The feature selection results showed that the Bior4.4 wavelet filter in HSV color space achieved greater accuracy than the other feature groups. The classification results indicate that only the DAGMSVM meets the requirement of time efficiency for the system. The results of this study suggest that the best classification model for fish species recognition is composed of a wavelet domain feature extractor with Bior4.4 wavelet filter in HSV color space and a one-against-one algorithm based DAGMSVM classifier. |
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| Keywords: | Fish species classification Smartphone Computer vision Feature selection |
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