Research on Fast Browsing for Massive Image

  • Fang WangEmail author
  • Ying Peng
  • Xiaoya Lu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 686)


The development and application of image data has the characteristics of high resolution, large data volume and so on. Research on how to take advantage of MapReduce to distributed processing efficient and fast is one of the focuses and hotspots in the field of massive image data management. To solve the above problems, combing efficient distributed programming and running frame provided by MapReduce model and image pyramid algorithm, proposing and realizing a distributed model for massive image data. Experiment expresses that this model is good performance in massive image’s browsing and rooming.


Massive image Distributed processing MapReduce Image pyramid 



This work was financially supported by the Fundamental Research Funds for the Central Universities, Southwest University for Nationalities (No. 2015NZYQN71).


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.College of Computer Science and Technology, Southwest University for NationalitiesChengduChina
  2. 2.Computer System Key Laboratory of the National Council for NationalitiesSouthwest University for NationalitiesChengduChina

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