Mobile visual search via hievarchical sparse coding

Mobile visual search is attracting much research attention recently. Existing works focus on addressing the limited capacity of wireless channel yet overlook its instability, thus is not adaptive to the change of channel capacity. In this paper, a novel image retrieval algorithm that is scalable to...

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Main Authors: Yang, Xiyu, Liu, Lianli, Qian, Xueming, Mei, Tao, SHEN, Jialie, QI, Tian
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Language:English
Published: Institutional Knowledge at Singapore Management University 2014
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Online Access:https://ink.library.smu.edu.sg/sis_research/2498
http://dx.doi.org/10.1109/ICME.2014.6890294
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spelling sg-smu-ink.sis_research-34972015-11-16T02:48:05Z Mobile visual search via hievarchical sparse coding Yang, Xiyu Liu, Lianli Qian, Xueming Mei, Tao SHEN, Jialie QI, Tian Mobile visual search is attracting much research attention recently. Existing works focus on addressing the limited capacity of wireless channel yet overlook its instability, thus is not adaptive to the change of channel capacity. In this paper, a novel image retrieval algorithm that is scalable to various channel condition is proposed. The proposed algorithm contains three contributions: (1) to achieve instant retrieval under various channel capacity, we adjust transmission load by sparseness instead of codebook size; (2) we introduce hierarchical sparse coding into our retrieval workflow, where original codebook is transformed into a tree-structured dictionary which implies elements' priority; (3) we propose transmission priority ranking schemes that is adaptive to specific query. Experiment results show that the proposed algorithm outperforms BoW and Lasso based algorithm under different parameter settings. Retrieval results under different channel limitation validate the scalability of our method. 2014-07-01T07:00:00Z text https://ink.library.smu.edu.sg/sis_research/2498 info:doi/10.1109/ICME.2014.6890294 http://dx.doi.org/10.1109/ICME.2014.6890294 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Databases and Information Systems
spellingShingle Databases and Information Systems
Yang, Xiyu
Liu, Lianli
Qian, Xueming
Mei, Tao
SHEN, Jialie
QI, Tian
Mobile visual search via hievarchical sparse coding
description Mobile visual search is attracting much research attention recently. Existing works focus on addressing the limited capacity of wireless channel yet overlook its instability, thus is not adaptive to the change of channel capacity. In this paper, a novel image retrieval algorithm that is scalable to various channel condition is proposed. The proposed algorithm contains three contributions: (1) to achieve instant retrieval under various channel capacity, we adjust transmission load by sparseness instead of codebook size; (2) we introduce hierarchical sparse coding into our retrieval workflow, where original codebook is transformed into a tree-structured dictionary which implies elements' priority; (3) we propose transmission priority ranking schemes that is adaptive to specific query. Experiment results show that the proposed algorithm outperforms BoW and Lasso based algorithm under different parameter settings. Retrieval results under different channel limitation validate the scalability of our method.
format text
author Yang, Xiyu
Liu, Lianli
Qian, Xueming
Mei, Tao
SHEN, Jialie
QI, Tian
author_facet Yang, Xiyu
Liu, Lianli
Qian, Xueming
Mei, Tao
SHEN, Jialie
QI, Tian
author_sort Yang, Xiyu
title Mobile visual search via hievarchical sparse coding
title_short Mobile visual search via hievarchical sparse coding
title_full Mobile visual search via hievarchical sparse coding
title_fullStr Mobile visual search via hievarchical sparse coding
title_full_unstemmed Mobile visual search via hievarchical sparse coding
title_sort mobile visual search via hievarchical sparse coding
publisher Institutional Knowledge at Singapore Management University
publishDate 2014
url https://ink.library.smu.edu.sg/sis_research/2498
http://dx.doi.org/10.1109/ICME.2014.6890294
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