Multimedia Tools and Applications

, Volume 78, Issue 1, pp 747–766 | Cite as

A common subgraph correspondence mining framework for map search services

  • Wu LiuEmail author
  • Lingheng Zhu
  • Lingyang Chu
  • Huadong Ma


With the development of GPS, Internet, and mobile devices, the map searching services become an essential application in people’s lives. However, existing map searching services only support simple keywords based location search, which neglect users’ complex search requirements, such as searching a hotel surrounded by station, shopping mall, cinema, etc. In this paper, we propose a map searching framework which maps users’ complex search requirements into a graph pattern match problem. In this framework, the user’s query requirements are mapped into a undirected graph, where the vertexes indicate the searched locations, and the edges represent the distance between connected locations. In this way, we propose a common pattern searching algorithm to give the top k matches groups and arrange them using a similarity. The similarity is defined by multi-model information, i.e., the graph structure, location categories, review stars, and review counts. Moreover, this method also allows user to input ambiguous and uncertain query condition with sketching query map. To adapt the method to large-scale data, we also filter the candidate groups by effective pruning methods. The evaluations on Yelp dataset demonstrate the proposed method is effective and flexibility in both certain and uncertain query graphs.


Map searching Graph pattern match Common pattern mining Multi-model information 



This work is partially supported by the Funds for International Cooperation and Exchange of the National Natural Science Foundation of China (No. 61720106007), the National Natural Science Foundation of China (No. 61602049), the NSFC-Guangdong Joint Fund (U1501254), and the Cosponsored Project of Beijing Committee of Education.


  1. 1.
    Cheng J, Yu JX, Ding B, Yu PS, Wang H (2008) Fast graph pattern matching. In: Proceedings of the 24th International Conference on Data Engineering, ICDE 2008, Cancún, pp 913–922Google Scholar
  2. 2.
    Chu L, Jiang S, Huang Q (2011) Fast common visual pattern detection via radiate geometric model. In: 18th IEEE International Conference on Image Processing, ICIP 2011, Brussels, Belgium, pp 2465– 2468Google Scholar
  3. 3.
    Chu L, Jiang S, Wang S, Zhang Y, Huang Q (2013) Robust spatial consistency graph model for partial duplicate image retrieval. IEEE Trans Multimed 15(8):1982–1996CrossRefGoogle Scholar
  4. 4.
    Chu L, Zhang Y, Li G, Wang S, Zhang W, Huang Q (2016) Effective multimodality fusion framework for cross-media topic detection. IEEE Trans Circ Syst Video Techn 26(3):556–569CrossRefGoogle Scholar
  5. 5.
    Gan C, Wang N, Yang Y, Yeung D, Hauptmann AG (2015) Devnet: a deep event network for multimedia event detection and evidence recounting. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 2568–2577Google Scholar
  6. 6.
    He H, Wang H, Yang J, Yu PS (2007) BLINKS: ranked keyword searches on graphs. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, Beijing, China, pp 305– 316Google Scholar
  7. 7.
    Horaud R, Skordas T (1989) Stereo correspondence through feature grouping and maximal cliques. IEEE Trans Pattern Anal Mach Intell 11(11):1168–1180CrossRefGoogle Scholar
  8. 8.
    Khan A, Li N, Yan X, Guan Z, Chakraborty S, Tao S (2011) Neighborhood based fast graph search in large networks. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2011, Athens, Greece, pp 901–912Google Scholar
  9. 9.
    Khan A, Wu Y, Aggarwal CC, Yan X (2013) Nema: fast graph search with label similarity. PVLDB 6(3):181–192Google Scholar
  10. 10.
    Liu H, Yan S (2010) Common visual pattern discovery via spatially coherent correspondences. In: The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2010, San Francisco, CA, USA, pp 1609–1616Google Scholar
  11. 11.
    Liu W, Zhang Y, Tang S, Tang J, Hong R, Li J (2013) Accurate estimation of human body orientation from RGB-D sensors. IEEE Trans Cybern 43 (5):1442–1452CrossRefGoogle Scholar
  12. 12.
    Liu W, Mei T, Zhang Y (2014) Instant mobile video search with layered audio-video indexing and progressive transmission. IEEE Trans Multimed 16 (8):2242–2255CrossRefGoogle Scholar
  13. 13.
    Liu W, Mei T, Zhang Y, Che C, Luo J (2015) Multi-task deep visual-semantic embedding for video thumbnail selection. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, pp 3707–3715Google Scholar
  14. 14.
    Liu A, Su Y, Jia P, Gao Z, Hao T, Yang Z (2015) Multipe/single-view human action recognition via part-induced multitask structural learning. IEEE Trans Cybern 45(6):1194–1208CrossRefGoogle Scholar
  15. 15.
    Liu A, Nie W, Gao Y, Su Y (2016) Multi-modal clique-graph matching for view-based 3d model retrieval. IEEE Trans Image Process 25(5):2103–2116MathSciNetCrossRefGoogle Scholar
  16. 16.
    Liu A, Su Y, Nie W, Kankanhalli MS (2017) Hierarchical clustering multi-task learning for joint human action grouping and recognition. IEEE Trans Pattern Anal Mach Intell 39(1):102–114CrossRefGoogle Scholar
  17. 17.
    Ma H, Liu W (2017) Progressive search paradigm for internet of things. IEEE Multimedia.
  18. 18.
    Motzkin TS, Straus EG (1965) Maxima for graphs and a new proof of a theorem of tur’an. Can J Math 17(17):533–540CrossRefGoogle Scholar
  19. 19.
    Nie W, Liu A, Gao Z, Su Y (2015) Clique-graph matching by preserving global andamp; local structure. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, pp 4503– 4510Google Scholar
  20. 20.
    Pavan M, Pelillo M (2007) Dominant sets and pairwise clustering. IEEE Trans Pattern Anal Mach Intell 29(1):167–172CrossRefGoogle Scholar
  21. 21.
    Pelillo M, Siddiqi K, Zucker SW (1999) Matching hierarchical structures using association graphs. IEEE Trans Pattern Anal Mach Intell 21(11):1105–1120CrossRefGoogle Scholar
  22. 22.
    Pėrez J, Arenas M, Gutierrez C (2009) Semantics and complexity of SPARQL. ACM Trans. Database Syst 34(3):16:1–16:45CrossRefGoogle Scholar
  23. 23.
    Sandholm WH (2009) Evolutionary game theory. Springer, New York, pp 3176–3205Google Scholar
  24. 24.
    Yang S, Wu Y, Sun H, Yan X (2014) Schemaless and structureless graph querying. PVLDB 7(7):565–576Google Scholar
  25. 25.
    Zhu L, Liu W, Chu L, Liu P, Gu X (2017) Query from sketch: a common subgraph correspondence mining framework. In: IEEE International Conference on Multimedia Big Data, pp 413–418Google Scholar
  26. 26.
    Zou L, Mo J, Chen L, Özsu MT, Zhao D (2011) gstore: answering SPARQL queries via subgraph matching. PVLDB 4(8):482–493Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Beijing Key Laboratory of Intelligent Telecommunications Software and MultimediaBeijing University of Posts and TelecommunicationsBeijingChina
  2. 2.School of Computing ScienceSimon Fraser UniversityVancouverCanada

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