A novel lifelong learning model based on cross domain knowledge extraction and transfer to classify underwater images

Artificial intelligence based autonomous systems interacting with dynamic environment are required to continuously learn, accumulate and improve the learned knowledge. Currently, most artificial intelligence based systems lack this ability and work in isolated learning paradigm. Human beings follow the continuous learning process by retaining and accumulating the learnt knowledge, and by using the learnt knowledge to solve the problem at hand. In this paper, we present a lifelong learning model, to solve challenging problem of real world underwater image classification. The proposed model is capable to learn from simple problems, accumulates the learnt knowledge by continual learning and uses the learnt knowledge to solve future complex problems of the same or related domain, in a similar way as humans do. In the proposed model, firstly, a deep classification convolutional autoencoder is presented to extract spatially localized features from images by utilizing convolution filters, then a code fragment based learning classifier system, with rich knowledge encoding scheme, is proposed for knowledge representation and transfer. In order to validate the model, experiments are conducted on two underwater images datasets and one in-air images dataset. Experiments results demonstrate that the proposed method outperforms base line method and state-of-the-art convolution neural network (CNN) methods. (C) 2020 Elsevier Inc. All rights reserved.

Automated summary

This paper presents a lifelong learning model to solve the challenging problem of real world underwater image classification. The model is capable of continuously learning, accumulating knowledge, and using it to solve future complex problems, similar to human learning process. Experimental results demonstrate that the proposed method outperforms baseline methods and state-of-the-art convolutional neural network (CNN) methods.