NDLI: Evaluation of visual object retrieval datasets

Content Provider	IEEE Xplore Digital Library
Author	Cai-Zhi Zhu Satoh, S.
Copyright Year	2013
Description	Author affiliation: Nat. Inst. of Inf., Tokyo, Japan (Cai-Zhi Zhu; Satoh, S.)
Abstract	Recently visual object retrieval has being widely studied for its vast application prospect, and the progress is usually tracked by the performance achieved on benchmark datasets. Therefore, in order to faithfully evaluate object retrieval algorithms, the quality of datasets must be ensured by some means. In this paper, we propose a method to evaluate the quality of benchmarking visual object retrieval on two highly cited object retrieval datasets, the Oxford datasets and TrecVid instance search datasets. Our evaluation method leverages the essential differences between object retrieval and other similar image search, and digs out some unrevealed and rather interesting features from those datasets. To the best of our knowledge, this research has never been touched before. Our method is believed to be beneficial to dataset collection and algorithm evaluation of object retrieval. More importantly, we hope this work can attract more attentions on this topic in the community.
Starting Page	3954
Ending Page	3958
File Size	2773223
Page Count	5
File Format	PDF
ISBN	9781479923410
DOI	10.1109/ICIP.2013.6738814
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-15
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	dataset design visual object retrieval similar image search dataset bias dataset evaluation
Content Type	Text
Resource Type	Article

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