NDLI: Noisy Tag Alignment with Image Regions

Content Provider	IEEE Xplore Digital Library
Author	Yang Liu Jing Liu Zechao Li Hanqing Lu
Copyright Year	2012
Abstract	With the permeation of Web 2.0, large-scale user contributed images with tags are easily available on social websites. How to align these social tags with image regions is a challenging task while no additional human intervention is considered, but a valuable one since the alignment can provide more detailed image semantic information and improve the accuracy of image retrieval. To this end, we propose a large margin discriminative model for automatically locating unaligned and possibly noisy image-level tags to the corresponding regions, and the model is optimized using concave-convex procedure (CCCP). In the model, each image is considered as a bag of segmented regions, associated with a set of candidate labeling vectors. Each labeling vector encodes a possible label arrangement for the regions of an image. To make the size of admissible labels tractable, we adopt an effective strategy based on the consistency between visual similarity and semantic correlation to generate a more compact set of labeling vectors. Extensive experiments on MSRC and SAIAPR TC-12 databases have been conducted to demonstrate the encouraging performance of our method comparing with other baseline methods.
Starting Page	266
Ending Page	271
File Size	927596
Page Count	6
File Format	PDF
ISBN	9781467316590
ISSN	19457871
DOI	10.1109/ICME.2012.143
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-09
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Labeling Vectors Semantics Visualization Accuracy Correlation Training Partially-supervised Learning Image Region Annotation
Content Type	Text
Resource Type	Article

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