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Experimental study of unsupervised feature learning for HEp-2 cell images clustering

Conference Paper


Abstract


  • Automatic identification of HEp-2 cell images has received an increasing research attention. Feature representations play a critical role in achieving good identification performance. Much recent work has focused on supervised feature learning. Typical methods consist of BoW model (based on hand-crafted features) and deep learning model (learning hierarchical features). However, these labels used in supervised feature learning are very labour-intensive and time-consuming. They are commonly manually annotated by specialists and very expensive to obtain. In this paper, we follow this fact and focus on unsupervised feature learning. We have verified and compared the features of these two typical models by clustering. Experimental results show the BoW model generally perform better than deep learning models. Also, we illustrate BoW model and deep learning models have complementarity properties.

Publication Date


  • 2014

Citation


  • Zhao, Y., Gao, Z., Wang, L. & Zhou, L. (2014). Experimental study of unsupervised feature learning for HEp-2 cell images clustering. Digital lmage Computing: Techniques and Applications (DlCTA), 2014 International Conference on (pp. 1-8). United States: IEEE.

Scopus Eid


  • 2-s2.0-84922496338

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/4063

Has Global Citation Frequency


Start Page


  • 1

End Page


  • 8

Place Of Publication


  • United States

Abstract


  • Automatic identification of HEp-2 cell images has received an increasing research attention. Feature representations play a critical role in achieving good identification performance. Much recent work has focused on supervised feature learning. Typical methods consist of BoW model (based on hand-crafted features) and deep learning model (learning hierarchical features). However, these labels used in supervised feature learning are very labour-intensive and time-consuming. They are commonly manually annotated by specialists and very expensive to obtain. In this paper, we follow this fact and focus on unsupervised feature learning. We have verified and compared the features of these two typical models by clustering. Experimental results show the BoW model generally perform better than deep learning models. Also, we illustrate BoW model and deep learning models have complementarity properties.

Publication Date


  • 2014

Citation


  • Zhao, Y., Gao, Z., Wang, L. & Zhou, L. (2014). Experimental study of unsupervised feature learning for HEp-2 cell images clustering. Digital lmage Computing: Techniques and Applications (DlCTA), 2014 International Conference on (pp. 1-8). United States: IEEE.

Scopus Eid


  • 2-s2.0-84922496338

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/4063

Has Global Citation Frequency


Start Page


  • 1

End Page


  • 8

Place Of Publication


  • United States