Fast semantic diffusion for large-scale context-based image and video annotation

Exploring context information for visual recognition has recently received significant research attention. This paper proposes a novel and highly efficient approach, which is named semantic diffusion, to utilize semantic context for large-scale image and video annotation. Starting from the initial a...

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Main Authors: JIANG, Yu-Gang, DAI, Qi, WANG, Jun, NGO, Chong-wah
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Language:English
Published: Institutional Knowledge at Singapore Management University 2012
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Online Access:https://ink.library.smu.edu.sg/sis_research/6322
https://ink.library.smu.edu.sg/context/sis_research/article/7325/viewcontent/TIP12_Context.pdf
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spelling sg-smu-ink.sis_research-73252021-11-23T05:10:48Z Fast semantic diffusion for large-scale context-based image and video annotation JIANG, Yu-Gang DAI, Qi WANG, Jun NGO, Chong-wah Exploring context information for visual recognition has recently received significant research attention. This paper proposes a novel and highly efficient approach, which is named semantic diffusion, to utilize semantic context for large-scale image and video annotation. Starting from the initial annotation of a large number of semantic concepts (categories), obtained by either machine learning or manual tagging, the proposed approach refines the results using a graph diffusion technique, which recovers the consistency and smoothness of the annotations over a semantic graph. Different from the existing graph-based learning methods that model relations among data samples, the semantic graph captures context by treating the concepts as nodes and the concept affinities as the weights of edges. In particular, our approach is capable of simultaneously improving annotation accuracy and adapting the concept affinities to new test data. The adaptation provides a means to handle domain change between training and test data, which often occurs in practice. Extensive experiments are conducted to improve concept annotation results using Flickr images and TV program videos. Results show consistent and significant performance gain (10 on both image and video data sets). Source codes of the proposed algorithms are available online. 2012-06-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6322 info:doi/10.1109/TIP.2012.2188038 https://ink.library.smu.edu.sg/context/sis_research/article/7325/viewcontent/TIP12_Context.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Context image and video annotation semantic concept semantic diffusion (SD) Graphics and Human Computer Interfaces
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Context
image and video annotation
semantic concept
semantic diffusion (SD)
Graphics and Human Computer Interfaces
spellingShingle Context
image and video annotation
semantic concept
semantic diffusion (SD)
Graphics and Human Computer Interfaces
JIANG, Yu-Gang
DAI, Qi
WANG, Jun
NGO, Chong-wah
Fast semantic diffusion for large-scale context-based image and video annotation
description Exploring context information for visual recognition has recently received significant research attention. This paper proposes a novel and highly efficient approach, which is named semantic diffusion, to utilize semantic context for large-scale image and video annotation. Starting from the initial annotation of a large number of semantic concepts (categories), obtained by either machine learning or manual tagging, the proposed approach refines the results using a graph diffusion technique, which recovers the consistency and smoothness of the annotations over a semantic graph. Different from the existing graph-based learning methods that model relations among data samples, the semantic graph captures context by treating the concepts as nodes and the concept affinities as the weights of edges. In particular, our approach is capable of simultaneously improving annotation accuracy and adapting the concept affinities to new test data. The adaptation provides a means to handle domain change between training and test data, which often occurs in practice. Extensive experiments are conducted to improve concept annotation results using Flickr images and TV program videos. Results show consistent and significant performance gain (10 on both image and video data sets). Source codes of the proposed algorithms are available online.
format text
author JIANG, Yu-Gang
DAI, Qi
WANG, Jun
NGO, Chong-wah
author_facet JIANG, Yu-Gang
DAI, Qi
WANG, Jun
NGO, Chong-wah
author_sort JIANG, Yu-Gang
title Fast semantic diffusion for large-scale context-based image and video annotation
title_short Fast semantic diffusion for large-scale context-based image and video annotation
title_full Fast semantic diffusion for large-scale context-based image and video annotation
title_fullStr Fast semantic diffusion for large-scale context-based image and video annotation
title_full_unstemmed Fast semantic diffusion for large-scale context-based image and video annotation
title_sort fast semantic diffusion for large-scale context-based image and video annotation
publisher Institutional Knowledge at Singapore Management University
publishDate 2012
url https://ink.library.smu.edu.sg/sis_research/6322
https://ink.library.smu.edu.sg/context/sis_research/article/7325/viewcontent/TIP12_Context.pdf
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