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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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 |
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Databases and Information Systems Yang, Xiyu Liu, Lianli Qian, Xueming Mei, Tao SHEN, Jialie QI, Tian Mobile visual search via hievarchical sparse coding |
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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. |
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Yang, Xiyu Liu, Lianli Qian, Xueming Mei, Tao SHEN, Jialie QI, Tian |
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Yang, Xiyu Liu, Lianli Qian, Xueming Mei, Tao SHEN, Jialie QI, Tian |
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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 |
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Mobile visual search via hievarchical sparse coding |
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Mobile visual search via hievarchical sparse coding |
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mobile visual search via hievarchical sparse coding |
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Institutional Knowledge at Singapore Management University |
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2014 |
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https://ink.library.smu.edu.sg/sis_research/2498 http://dx.doi.org/10.1109/ICME.2014.6890294 |
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