Fish species classification using a collaborative technique of firefly algorithm and neural network

Abstract

Monitoring various Fish Species and its distribution of the species obtains a primary significance in receiving the insights to marine ecological-system. After this, visual classification of those species would aid in tracing out the movement and yield the patterns and trends in fish activities, which provides in depth knowledge of the species. Unconstrained under-water images pose highly variations because of the fish orientation changes, Light-intensities, similarity in fish patterns and fish shapes. This would create the greater challenge for Image-processing techniques in accurate classification of Fish species or the Fish classes. Hence, for this reason, Underwater Image Enhancement is implemented in combination of Morphological-operations in pre-processing method. The pre-processed image is then subjected to feature extraction process by using Speed-up Robust Feature algorithm. This is followed by Firefly Algorithm, applied for optimization of Region of interest selection in the selected-features. For the categorization of Fish-species, PatternNet is a technique which is employed, in classifying 10,000 marine fish-images to five categories (Dascyllus reticulatus, Plectroglyphidodon dickii, Chromis chrysura, Amphiprion clarkii, and Chaetodon lunulatus). The Efficiency of the proposed-framework is performed in terms of Classification accuracy, execution time, precision value, F-measure and recall factors with respect to various categories of fish species. The comparison of the proposed-framework is also assessed with the other existing methods. 98% of accuracy rate in classification was produced by the evaluation results of the proposed framework with a lesser average computation time of 3.64 s upon different tested images. Thus, the higher efficiency of the proposed framework is proved by the outcomes of the study.