Artificial Intelligent Systems and Machine Learning

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Fish Detection and Classification Using SOM


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1 Department of ISE, R.V.C.E, Bengaluru, India
     

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Fish species detection, classification and study have been an integral part of marine science research. Processing of fish images require methods having various visual techniques, one of the implementation is pattern recognition. The best technique used are the neural networks. Neural network is a “connectionist” computational system. Using these methods an effective system is built for recognition and classification of fish. The goal of the project is to recognize fish based on the texture. There are mainly four features taken into consideration for detection and they are contrast, energy, homogeneity, correlation. For classification unsupervised neural network self-organized map is used, which gives the output as different classes of fish. The system accepts input as a set of images. The output of the system is SOM-plots. The SOM plots are hexagonal neurons. Each neuron consists of the number of images belonging to their respective class. The system can be used by marine experts and for fish research to distinguish between fish species and also has medicinal benefits, considering the medicinal values of fish.


Keywords

Self-Organizing Maps, Edge-Detection, Texture Based Fish Detection, Vectorization.
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  • Pragati Shrivastava, Piyush Singh, Gaurav Shrivastava “Image Classification using SOM and SVM Feature Extraction” International Journal of Computer Science and Information Technologies, Vol. 5

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  • Fish Detection and Classification Using SOM

Abstract Views: 289  |  PDF Views: 4

Authors

S. K. Komal
Department of ISE, R.V.C.E, Bengaluru, India
B. S. Rekha
Department of ISE, R.V.C.E, Bengaluru, India
G. N. Srinivasan
Department of ISE, R.V.C.E, Bengaluru, India

Abstract


Fish species detection, classification and study have been an integral part of marine science research. Processing of fish images require methods having various visual techniques, one of the implementation is pattern recognition. The best technique used are the neural networks. Neural network is a “connectionist” computational system. Using these methods an effective system is built for recognition and classification of fish. The goal of the project is to recognize fish based on the texture. There are mainly four features taken into consideration for detection and they are contrast, energy, homogeneity, correlation. For classification unsupervised neural network self-organized map is used, which gives the output as different classes of fish. The system accepts input as a set of images. The output of the system is SOM-plots. The SOM plots are hexagonal neurons. Each neuron consists of the number of images belonging to their respective class. The system can be used by marine experts and for fish research to distinguish between fish species and also has medicinal benefits, considering the medicinal values of fish.


Keywords


Self-Organizing Maps, Edge-Detection, Texture Based Fish Detection, Vectorization.

References