NDLI: Robustness comparison of clustering — Based vs. non-clustering multi-label classifications for image and video annotations

Content Provider	IEEE Xplore Digital Library
Author	Nasierding, Gulisong Li, Yong Sajjanhar, Atul
Copyright Year	2015
Description	Author affiliation: School of Computer Science and Technology, Xinjiang Normal University, No. 102 Xin Yi Rd, Urumqi, China 830001 (Nasierding, Gulisong; Li, Yong) \|\| School of Information Technology, Deakin University, 221 Burwood HWY, Burwood, VIC 3125, Australia (Sajjanhar, Atul)
Abstract	This paper reports robustness comparison of clustering-based multi-label classification methods versus non-clustering counterparts for multi-concept associated image and video annotations. In the experimental setting of this paper, we adopted six popular multi-label classification algorithms, two different base classifiers for problem transformation based multi-label classifications, and three different clustering algorithms for pre-clustering of the training data. We conducted experimental evaluation on two multi-label benchmark datasets: scene image data and mediamill video data. We also employed two multi-label classification evaluation metrics, namely, micro F1-measure and Hamming-loss to present the predictive performance of the classifications. The results reveal that different base classifiers and clustering methods contribute differently to the performance of the multi-label classifications. Overall, the pre-clustering methods improve the effectiveness of multi-label classifications in certain experimental settings. This provides vital information to users when deciding which multi-label classification method to choose for multiple-concept associated image and video annotations.
Starting Page	691
Ending Page	696
File Size	799512
Page Count	6
File Format	PDF
e-ISBN	9781467390989
DOI	10.1109/CISP.2015.7407966
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Measurement Image segmentation Semantics Clustering algorithms multi-concept clustering based Prediction algorithms robustness comparison Classification algorithms multi-label classification image and video annotation
Content Type	Text
Resource Type	Article

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