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BioMed Research International
Volume 2016, Article ID 9406259, 14 pages
Research Article

Convolutional Deep Belief Networks for Single-Cell/Object Tracking in Computational Biology and Computer Vision

1Department of Computer Science and Engineering, Huaqiao University, Xiamen, China
2School of Information Science and Technology, Xiamen University, Xiamen, China

Received 1 June 2016; Revised 14 August 2016; Accepted 14 September 2016

Academic Editor: Dariusz Mrozek

Copyright © 2016 Bineng Zhong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In this paper, we propose deep architecture to dynamically learn the most discriminative features from data for both single-cell and object tracking in computational biology and computer vision. Firstly, the discriminative features are automatically learned via a convolutional deep belief network (CDBN). Secondly, we design a simple yet effective method to transfer features learned from CDBNs on the source tasks for generic purpose to the object tracking tasks using only limited amount of training data. Finally, to alleviate the tracker drifting problem caused by model updating, we jointly consider three different types of positive samples. Extensive experiments validate the robustness and effectiveness of the proposed method.