%0 Journal Article
%@ 21693536
%A Usmani, U.A.
%A Watada, J.
%A Jaafar, J.
%A Aziz, I.A.
%A Roy, A.
%D 2021
%F scholars:15595
%I Institute of Electrical and Electronics Engineers Inc.
%J IEEE Access
%K Deep learning; Learning algorithms; Pixels; Reinforcement learning; Semantic Web; Semantics; Statistical tests; Uncertainty analysis, 'current; Active Learning; Deep query network; Image pixels; Intelligence models; Object class; Query networks; Reinforcement learnings; Semantic segmentation; Vision based, Semantic Segmentation
%P 168415-168432
%R 10.1109/ACCESS.2021.3136647
%T A Reinforced Active Learning Algorithm for Semantic Segmentation in Complex Imaging
%U https://khub.utp.edu.my/scholars/15595/
%V 9
%X Semantic segmentation annotation helps train computer vision based Artificial Intelligence models where each image pixel is assigned to a specific object class. The model developers try to identify the features helpful for determining the objects of interest by using various supervised deep learning techniques. However, this is a difficult task due to the complexity of object structures. Two difficulties arise in the current approaches for semantic segmentation. The pixel-wise label approach is costly to obtain and is time consuming. Second, the datasets taken for the semantic segmentation task are not balanced since certain classes are present more than the others. This biases the model performance to the most represented ones. We propose a new reinforced active learning strategy based on a deep reinforcement learning algorithm. This work presents a modified Deep Q Learning formulation for active learning. An agent learns the strategy of selecting a subset of small image regions, which are more knowledgeable than the whole set of images from an unlabeled data pool. The decision on the area of selection is dependent on the assumptions and segmentation model uncertainties taken for training purposes. We use the CamVid and RGB indoor test scenes dataset to evaluate the proof of concept. Our results infer that our approach demands more labels from under-represented groups than the baselines, thus enhancing their efficiency and mitigating the class imbalance. Our method's performance is superior to the conventional deep learning models in detecting 8 out of 11 classes on the Camvid road segmentation scene dataset. It achieves an accuracy of 90.56, a mIoU score of 87.17, and a BF score of 93.14. On the SUNRGB indoor scenes dataset, it gives an accuracy of around 75.82 and a BF score of 77.25, thus outperforming the current state-of-the-art methods. © 2013 IEEE.
%Z cited By 6