Multimedia Tools and Applications

, Volume 76, Issue 8, pp 10939–10958 | Cite as

A car-face region-based image retrieval method with attention of SIFT features

  • Changyou Zhang
  • Xiaoya Wang
  • Jun Feng
  • Yu Cheng
  • Cheng Guo
Article
  • 196 Downloads

Abstract

A traffic still image captured by the high-definition camera located in traffic block port often contains several vehicles. It provides an important clue to solve vehicle crime cases to retrieve all these images that contain the given type of car. To enhance the performance of image retrieval, we proposed a car-face region-based image retrieval method with the attention of SIFT features. In our method, the first step is to find all the car-face regions from an original traffic image, and this original image is represented as a set of car-face regions. Secondly, a similarity measure metrics is proposed with a light intense training of the attention value of SIFT key points on a very small identified images set. Finally, according to the similarity between the input region and the target region, all the images with at least one similar region are retrieved. We carry out this method on a training set of 100 positive car-face region-images. Compared with the famous training-based SVM method, our method achieved higher precision at the same recall with lower training intensity.

Keywords

Car-face image Region-based retrieval Attention SIFT features Similarity 

Notes

Acknowledgments

This paper is supported by the Natural Science Foundation of China (61379048), Hebei Province Natural Science Foundation (F2013210109). Special Project of National CAS Union – “The High Performance Cloud Service Platform for Enterprise Creative Computing”, Special Project of National CAS Union – “Car Image Retrieval on Many-core platforms”, Project of “Research and development of data mining technology in the operation of the wind turbine”. The authors also would like to express appreciation to the anonymous reviewers for their helpful comments on improving the paper.

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Changyou Zhang
    • 1
    • 2
  • Xiaoya Wang
    • 2
  • Jun Feng
    • 2
  • Yu Cheng
    • 3
  • Cheng Guo
    • 4
  1. 1.Laboratory of Parallel Software and Computational Science, Institute of SoftwareChinese Academy of SciencesBeijingChina
  2. 2.School of Infomatics Science and TechnologyShijiazhuang Tiedao UniversityShijiazhuangChina
  3. 3.Institute of Applied MathematicsHebei Academy of SciencesShijiazhuangChina
  4. 4.Yunnan Electric Power Research InstituteYunnan Power Grid Co., Ltd.KunmingChina

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