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A compressed sensing approach for query by example video retrieval

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Abstract

Recently, compressed Sensing (CS) has theoretically been proposed for more efficient signal compression and recovery. In this paper, the CS based algorithms are investigated for Query by Example Video Retrieval (QEVR) and a novel similarity measure approach is proposed. Combining CS theory with the traditional discrete cosine transform (DCT), better compression efficiency for spatially sparse is achieved. The similarity measure from three levels (frame level, shot level and video level, respectively) is also discussed. For several different kinds of natural videos, the experimental results demonstrate the effectiveness of system by the proposed method.

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Acknowledgment

This work was supported by National Natural Science Foundation of China (Grant No. 61103114 and 61004112).

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Authors and Affiliations

  1. College of Computer Science, Chongqing University, Chongqing, 400030, China

    Sujuan Hou, Shangbo Zhou & Muhammad Abubakar Siddique

  2. Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing University, Chongqing, 400030, China

    Sujuan Hou & Shangbo Zhou

Authors
  1. Sujuan Hou
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  2. Shangbo Zhou
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  3. Muhammad Abubakar Siddique
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Correspondence to Shangbo Zhou.

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Hou, S., Zhou, S. & Siddique, M.A. A compressed sensing approach for query by example video retrieval. Multimed Tools Appl 72, 3031–3044 (2014). https://doi.org/10.1007/s11042-013-1573-y

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