Enhancing learning classifier systems through convolutional autoencoder to classify underwater images

Abstract

Underwater image classification is a challenging task because of challenging underwater environment and lighting conditions. We propose a novel learning classifier system (LCS), which can classify large-size underwater images with promising accuracy, and acquire knowledge in interpretable format, by using a novel classification convolution autoencoder (CCAE). In proposed system, CCAE is designed as a hybrid network, which combines benefits of classification and autoencoder, to extract compressed non-trivial features. It is also used to decompress LCS generated rules to original input space. In order to evaluate effectiveness of proposed solution, experiments are conducted on selected underwater synsets of benchmark ImageNet dataset. Results are compared with famous CNN methods based on parameters such as accuracy, precision, recall and F-measure. Experiments show that proposed system can accurately classify large-size underwater images with promising accuracy and outperforms well-known deep CNN methods. It has also been observed that LCS generated rules are well generalized, accurate and interpretable.