New Workflow for Marine Fish Classification Based on Combination Features and CLAHE Enhancement Technique

Ricardus Anggi Pramunendar, Dwi Puji Prabowo, Dewi Pergiwati, Yuslena Sari, Pulung Nurtantio Andono, and Moch Arief Soeleman.

International Journal of Intelligent Engineering & Systems (IJIES), vol.13, no.4, 2020, pp. 293-304.

Abstract: Automatic identification of fish species is very complex and challenging because of the low quality of the marine environment. Thus, the identification of fish species using computer vision technology is disrupted. However, various researchers only focus on determining the best fish identification method without considering the quality of the data used. Therefore, this study presented a new workflow in identifying fish species. A combination of feature extraction methods and a backpropagation neural network (BPNN) method was used, which was based on image quality improvement techniques using contrast limited adaptive histogram equalization (CLAHE) with adaptive threshold by fuzzy c-means. This study compared the results of fish identification on the original data and image data that were enhanced using several classifications of machine learning. The results show that data with improved quality of the images will improve accuracy for fish species identification and improvement using the proposed method of 3.56%. This could support the reduction of invasive fish populations through automated fish identification systems in unrestricted natural environments based on computer vision technology.

Keywords: Image enhancement, Fish identification, NCACC, GLCM, Neural network

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