TY - JOUR
T1 - Semantic segmentation using Firefly Algorithm-based evolving ensemble deep neural networks
AU - Zhang, Li
AU - Slade, Sam
AU - Lim, Chee Peng
AU - Asadi, Houshyar
AU - Nahavandi, Saeid
AU - Huang, Haoqian
AU - Ruan, Hang
N1 - Funding information: This research is supported in part by Innovate UK Smart Grants (Grant ID: 10032047).
PY - 2023/10/9
Y1 - 2023/10/9
N2 - Automatic segmentation of salient objects in real-world images has gained increasing interests owing to its popularity in diverse real-world applications, such as autonomous driving, medical diagnosis, aviation security, and underwater surveillance. In this research, we propose Firefly Algorithm (FA)-enhanced evolving ensemble deep networks for semantic segmentation and visual saliency prediction. An improved FA model is proposed to optimize network hyper-parameters. Specifically, it employs mutation operators and a neighbouring search strategy with granular search steps to establish search intensification. It also emphasizes search diversification by adopting multiple dynamic hybrid leaders and diverse adaptive sine and cosine search trajectories in full and randomly selected sub-dimensions to overcome stagnation. Because of its competent segmentation performance, DeepLabV3+ is fine-tuned using transfer learning with FA-based hyper-parameter identification. We optimize the learning rate, momentum and weight decay of the transfer learning network. A number of optimized DeepLabV3+ networks with distinguishing learning configurations are yielded. An ensemble model is subsequently constructed by incorporating three optimized base networks to further strengthen segmentation performance. Evaluated using diverse challenging semantic segmentation and saliency prediction tasks using underwater and medical image data sets, our evolving ensemble deep network illustrates significant superiority over other state-of-the-art deep networks and existing studies. The proposed FA model also outperforms other search methods in solving diverse mathematical landscapes with statistical significance.
AB - Automatic segmentation of salient objects in real-world images has gained increasing interests owing to its popularity in diverse real-world applications, such as autonomous driving, medical diagnosis, aviation security, and underwater surveillance. In this research, we propose Firefly Algorithm (FA)-enhanced evolving ensemble deep networks for semantic segmentation and visual saliency prediction. An improved FA model is proposed to optimize network hyper-parameters. Specifically, it employs mutation operators and a neighbouring search strategy with granular search steps to establish search intensification. It also emphasizes search diversification by adopting multiple dynamic hybrid leaders and diverse adaptive sine and cosine search trajectories in full and randomly selected sub-dimensions to overcome stagnation. Because of its competent segmentation performance, DeepLabV3+ is fine-tuned using transfer learning with FA-based hyper-parameter identification. We optimize the learning rate, momentum and weight decay of the transfer learning network. A number of optimized DeepLabV3+ networks with distinguishing learning configurations are yielded. An ensemble model is subsequently constructed by incorporating three optimized base networks to further strengthen segmentation performance. Evaluated using diverse challenging semantic segmentation and saliency prediction tasks using underwater and medical image data sets, our evolving ensemble deep network illustrates significant superiority over other state-of-the-art deep networks and existing studies. The proposed FA model also outperforms other search methods in solving diverse mathematical landscapes with statistical significance.
KW - Evolving ensemble segmenter
KW - Firefly Algorithm
KW - Image segmentation
UR - http://www.scopus.com/inward/record.url?scp=85166917550&partnerID=8YFLogxK
U2 - 10.1016/j.knosys.2023.110828
DO - 10.1016/j.knosys.2023.110828
M3 - Article
AN - SCOPUS:85166917550
SN - 0950-7051
VL - 277
JO - Knowledge-Based Systems
JF - Knowledge-Based Systems
M1 - 110828
ER -