Please use this identifier to cite or link to this item: https://doi.org/10.1145/1631272.1631291
Title: Label to region by bi-layer sparsity priors
Authors: Liu, X. 
Cheng, B.
Yan, S. 
Tang, J. 
Chua, T.S. 
Jin, H.
Keywords: Bi-layer sparse coding
Image annotation
Image parsing
Label-to-region assignment
Issue Date: 2009
Source: Liu, X.,Cheng, B.,Yan, S.,Tang, J.,Chua, T.S.,Jin, H. (2009). Label to region by bi-layer sparsity priors. MM'09 - Proceedings of the 2009 ACM Multimedia Conference, with Co-located Workshops and Symposiums : 115-124. ScholarBank@NUS Repository. https://doi.org/10.1145/1631272.1631291
Abstract: In this work, we investigate how to automatically reassign the manually annotated labels at the image-level to those contextually derived semantic regions. First, we propose a bi-layer sparse coding formulation for uncovering how an image or semantic region can be robustly reconstructed from the over-segmented image patches of an image set. We then harness it for the automatic label to region assignment of the entire image set. The solution to bi-layer sparse coding is achieved by convex ℓ1-norm minimization. The underlying philosophy of bi-layer sparse coding is that an image or semantic region can be sparsely reconstructed via the atomic image patches belonging to the images with common labels, while the robustness in label propagation requires that these selected atomic patches come from very few images. Each layer of sparse coding produces the image label assignment to those selected atomic patches and merged candidate regions based on the shared image labels. The results from all bi-layer sparse codings over all candidate regions are then fused to obtain the entire label to region assignments. Besides, the presenting bi-layer sparse coding framework can be naturally applied to perform image annotation on new test images. Extensive experiments on three public image datasets clearly demonstrate the effectiveness of our proposed framework in both label to region assignment and image annotation tasks. Copyright 2009 ACM.
Source Title: MM'09 - Proceedings of the 2009 ACM Multimedia Conference, with Co-located Workshops and Symposiums
URI: http://scholarbank.nus.edu.sg/handle/10635/83879
ISBN: 9781605586083
DOI: 10.1145/1631272.1631291
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

59
checked on Feb 21, 2018

Page view(s)

78
checked on Feb 17, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.