Please use this identifier to cite or link to this item:
|Title:||Annotating web images using NOVA: NOn-conVex group spArsity||Authors:||Wu, F.
group feature selection
non-convex group sparsity
|Issue Date:||2012||Citation:||Wu, F.,Yuan, Y.,Rui, Y.,Yan, S.,Zhuang, Y. (2012). Annotating web images using NOVA: NOn-conVex group spArsity. MM 2012 - Proceedings of the 20th ACM International Conference on Multimedia : 509-518. ScholarBank@NUS Repository. https://doi.org/10.1145/2393347.2393419||Abstract:||As image feature vector is large, selecting the right features plays a fundamental role in Web image annotation. Most existing approaches are either based on individual feature selection, which leads to local optima, or using a convex penalty, which leads to inconsistency. To address these difficulties, in this paper we propose a new sparsity-based approach NOVA (NOn-conVex group spArsity). To the best of our knowledge, NOVA is the first to introduce non-convex penalty for group selection in high-dimensional heterogeneous features space. Because it is a group-sparsity approach, it approximately reaches global optima. Because it uses non-convex penalty, it achieves the consistency. We demonstrate the superior performance of NOVA via three means. First, we present theoretical proof that NOVA is consistent, satisfying un-biasness, sparsity and continuity. Second, we show NOVA converges to the true underlying model by using a ground-truth-available generative-model simulation. Third, we report extensive experimental results on three diverse and widely-used data sets Kodak, MSRA-MM 2.0, and NUS-WIDE. We also compare NOVA against the state-of-the-art approaches, and report superior experimental results. © 2012 ACM.||Source Title:||MM 2012 - Proceedings of the 20th ACM International Conference on Multimedia||URI:||http://scholarbank.nus.edu.sg/handle/10635/83494||ISBN:||9781450310895||DOI:||10.1145/2393347.2393419|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Apr 21, 2019
checked on Apr 18, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.