Deep learning

Pattern recognition (psychology)

Biology

Machine learning

Engineering

Artificial intelligence

Systems engineering

Computer science

Task (project management)

Fishery

Fish <Actinopterygii>

Artificial neural network

Convolutional neural network

Segmentation

In recent days, a lot of study has been made in classification and recognition of an image. It becomes a challenging task due to some factors such as segmentation errors, distortion, noise, overlapping of images and the most crucial aspect in fisheries is the recognition of live fish. So, to overcome the above said problem and also to classify whether the fish is a breeding fish or not, an approach has been proposed. Several videos are captured from the fisheries and the videos are converted into 900 frames. A deep learning and machine learning classifiers have been tried for classification of breeding and non-breeding fish. Convolution Neural Network (CNN) which is one of the deep learning techniques has been used for classifying the fish. In machine learning, the frames are first segmented and pre-processed and the features are extracted from the pre-processed and segmented frames. The extracted features are then used for classification. For classifying the fish several machine learning classifiers are used. Several dimensionality reduction techniques and ensemble methods have also been tried. Finally, the comparison results prove that CNN gives a better accuracy for classifying the breeding and non-breeding fish.

Classification of Breeding Fish using Deep Learning from the Captured Video