Please use this identifier to cite or link to this item:
|Title:||Label-specific training set construction from web resource for image annotation||Authors:||Tang, J.
Training set construction
|Issue Date:||Aug-2013||Citation:||Tang, J., Yan, S., Zhao, C., Chua, T.-S., Jain, R. (2013-08). Label-specific training set construction from web resource for image annotation. Signal Processing 93 (8) : 2199-2204. ScholarBank@NUS Repository. https://doi.org/10.1016/j.sigpro.2012.05.003||Abstract:||Recently many research efforts have been devoted to image annotation by leveraging on the associated tags/keywords of web images as training labels. A key issue to resolve is the relatively low accuracy of the tags. In this paper, we propose a novel semi-automatic framework to construct a more accurate and effective training set from these web media resources for each label that we want to learn. Locality sensitive Hashing (LSH) is applied to find the most possible region candidates of a given label efficiently. We further conduct simple human interactions to approve whether the clusters of region candidates are relevant to the given label. Here Hashing ensures the efficiency and the minimal human efforts guarantee the effectiveness of the proposed framework. Experiments conducted on a real-world dataset demonstrate that the constructed training set can result in higher accuracy for image annotation. © 2012 Elsevier B.V.||Source Title:||Signal Processing||URI:||http://scholarbank.nus.edu.sg/handle/10635/50963||ISSN:||01651684||DOI:||10.1016/j.sigpro.2012.05.003|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Feb 3, 2023
WEB OF SCIENCETM
checked on Jan 26, 2023
checked on Feb 2, 2023
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.