  Within the realm of image recognition, a specific category of multi-label
classification (MLC) challenges arises when objects within the visual field may
occlude one another, demanding simultaneous identification of both occluded and
occluding objects. Traditional convolutional neural networks (CNNs) can tackle
these challenges; however, those models tend to be bulky and can only attain
modest levels of accuracy. Leveraging insights from cutting-edge neural science
research, specifically the Holistic Bursting (HB) cell, this paper introduces a
pioneering integrated network framework named HB-net. Built upon the foundation
of HB cell clusters, HB-net is designed to address the intricate task of
simultaneously recognizing multiple occluded objects within images. Various
Bursting cell cluster structures are introduced, complemented by an evidence
accumulation mechanism. Testing is conducted on multiple datasets comprising
digits and letters. The results demonstrate that models incorporating the HB
framework exhibit a significant $2.98\%$ enhancement in recognition accuracy
compared to models without the HB framework ($1.0298$ times, $p=0.0499$).
Although in high-noise settings, standard CNNs exhibit slightly greater
robustness when compared to HB-net models, the models that combine the HB
framework and EA mechanism achieve a comparable level of accuracy and
resilience to ResNet50, despite having only three convolutional layers and
approximately $1/30$ of the parameters. The findings of this study offer
valuable insights for improving computer vision algorithms. The essential code
is provided at https://github.com/d-lab438/hb-net.git.
