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Efficient continuous top-k geo-image search on road network

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Abstract

With the rapid development of mobile Internet and cloud computing technology, large-scale multimedia data, e.g., texts, images, audio and videos have been generated, collected, stored and shared. In this paper, we propose a novel query problem named continuous top-k geo-image query on road network which aims to search out a set of geo-visual objects based on road network distance proximity and visual content similarity. Existing approaches for spatial textual query and geo-image query cannot address this problem effectively because they do not consider both of visual content similarity and road network distance proximity on road network. In order to address this challenge effectively and efficiently, firstly we propose the definition of geo-visual objects and continuous top-k geo-visual objects query on road network, then develop a score function for search. To improve the query efficiency in a large-scale road network, we propose the search algorithm named geo-visual search on road network based on a novel hybrid indexing framework called VIG-Tree, which combines G-Tree and visual inverted index technique. In addition, an important notion named safe interval and results updating rule are proposed, and based on them we develop an efficient algorithm named moving monitor algorithm to solve continuous query. Experimental evaluation on real multimedia dataset and road network dataset illustrates that our solution outperforms state-of-the-art method.

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References

  1. Alsubaiee S, Behm A, Li C (2010) Supporting location-based approximate-keyword queries. In: Proceedings of the 18th ACM SIGSPATIAL international symposium on advances in geographic information systems, ACM-GIS 2010. San Jose, pp 61–70

  2. Cary A, Wolfson O, Rishe N (2010) Efficient and scalable method for processing top-k spatial boolean queries. In: Proceedings of the 22nd international conference on scientific and statistical database management, SSDBM 2010. Heidelberg, pp 87–95

  3. Chen C, Chen C, Sun W (2013) Spatial keyword queries in wireless broadcast environment. In: Journal of computer research and development

  4. Christoforaki M, He J, Dimopoulos C, Markowetz A, Suel T (2011) Text vs. space: efficient geo-search query processing. In: Proceedings of the 20th ACM conference on information and knowledge management, CIKM 2011. Glasgow, pp 423–432

  5. Chum O, Philbin J, Zisserman A (2008) Near duplicate image detection: min-hash and tf-idf weighting. In: Proceedings of the British Machine Vision Conference 2008. Leeds, pp 1–10

  6. Cong G, Jensen CS, Wu D (2009) Efficient retrieval of the top-k most relevant spatial web objects. PVLDB 2(1):337–348

    Google Scholar 

  7. Fagin R, Lotem A, Naor M (2003) Optimal aggregation algorithms for middleware. J Comput Syst Sci 66(4):614–656

    Article  MathSciNet  Google Scholar 

  8. Fang H, Zhao P, Sheng VS, Wu J, Xu J, Liu A, Cui Z (2015) Effective spatial keyword query processing on road networks. In: Databases theory and applications - 26th australasian database conference, ADC 2015, melbourne, VIC. Proceedings, Australia, pp 194–206

    Google Scholar 

  9. Felipe ID, Hristidis V, Rishe N (2008) Keyword search on spatial databases. In: Proceedings of the 24th international conference on data engineering, ICDE 2008. Cancún, pp 656–665

  10. Fu R, Li B, Gao Y, Wang P (2016) Content-based image retrieval based on cnn and svm. In: Proceedings of 2nd IEEE international conference on computer and communications

  11. Gao Y, Qin X, Zheng B, Chen G (2015) Efficient reverse top-k boolean spatial keyword queries on road networks. IEEE Trans Knowl Data Eng 27(5):1205–1218

    Article  Google Scholar 

  12. Guo L, Shao J, Aung HH, Tan K (2015) Efficient continuous top-k spatial keyword queries on road networks. GeoInformatica 19(1):29–60

    Article  Google Scholar 

  13. Hariharan R, Hore B, Li C, Mehrotra S (2007) Processing spatial-keyword (SK) queries in geographic information retrieval (GIR) systems. In: Proceedings of the 19th international conference on scientific and statistical database management, SSDBM 2007. Banff, pp 16

  14. Hjaltason GR, Samet H (1999) Distance browsing in spatial databases. ACM Trans Database Syst 24(2):265–318

    Article  Google Scholar 

  15. Huang W, Li G, Tan K, Feng J (2012) Efficient safe-region construction for moving top-k spatial keyword queries. In: 21st ACM international conference on information and knowledge management, CIKM’12. Maui, pp 932–941

  16. Ke Y, Sukthankar R (2004) PCA-SIFT: A more distinctive representation for local image descriptors. In: 2004 IEEE computer society conference on computer vision and pattern recognition (CVPR 2004), with CD-ROM. Washington, pp 506–513

  17. Irtaza A, Jaffar MA, Muhammad MS (2015) Content based image retrieval in a web 3.0 environment. Multimed Tools Appl 74(14):5055–5072

    Article  Google Scholar 

  18. Li Z, Lee KCK, Zheng B, Lee W, Lee DL, Wang X (2011) Ir-tree: an efficient index for geographic document search. IEEE Trans Knowl Data Eng 23 (4):585–599

    Article  Google Scholar 

  19. Li Y, Li G, Zhang C (2013) Processing continuous top-k spatial keyword queries over road networks. In: J.huazhong univ. of sci. and t ECH. (natural science edition), pp 29–60

  20. Li C, Gu Y, Qi J, Yu G, Zhang R, Yi W (2014) Processing moving knn queries using influential neighbor sets. PVLDB 8(2):113–124

    Google Scholar 

  21. Lin X, Xu J, Hu H (2016) Reverse keyword search for spatio-textual top-k queries in location-based services. In: 32nd IEEE international conference on data engineering, ICDE 2016. Helsinki, pp 1488–1489

  22. Liu Y, Liu S, Wang Z (2015) Multi-focus image fusion with dense SIFT. Inf Fusion 23:139–155

    Article  Google Scholar 

  23. Lowe DG (1999) Object recognition from local scale-invariant features. In: ICCV, pp 1150–1157

  24. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  25. Luo C, Li J, Li G, Wei W, Li Y, Li J (2016) Efficient reverse spatial and textual k nearest neighbor queries on road networks. Knowl-Based Syst 93:121–134

    Article  Google Scholar 

  26. Mortensen EN, Deng H, Shapiro LG (2005) A SIFT descriptor with global context. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR 2005). San Diego, pp 184–190

  27. Norouzi M, Fleet DJ, Salakhutdinov R (2012) Hamming distance metric learning. In: Advances in neural information processing systems 25: 26th annual conference on neural information processing systems 2012. Proceedings of a meeting held December 3-6, Lake Tahoe, pp 1070–1078

  28. Rocha-Junior JB, Gkorgkas O, Jonassen S, Nørvåg K (2011) Efficient processing of top-k spatial keyword queries. In: Proceedings of the 12th international symposium on advances in spatial and temporal databases SSTD 2011 Minneapolis, pp 205–222

    Chapter  Google Scholar 

  29. Rocha-Junior JB, Nørvåg K (2012) Top-k spatial keyword queries on road networks. In: Proceedings of the 15th international conference on extending database technology, EDBT ’12. Berlin, pp 168– 179

  30. Sivic J, Zisserman A (2003) Video google: a text retrieval approach to object matching in videos. In: 9th IEEE international conference on computer vision (ICCV 2003). Nice, pp 1470–1477

  31. Thomee B, Lew MS (2012) Interactive search in image retrieval: a survey. IJMIR 1(2):71–86

    Google Scholar 

  32. Wang Y, Lin X, Zhang Q (2013) Towards metric fusion on multi-view data: a cross-view based graph random walk approach. In: 22Nd ACM international conference on information and knowledge management, CIKM’13. San Francisco, pp 805–810

  33. Wang Y, Lin X, Zhang Q, Wu L (2014) Shifting hypergraphs by probabilistic voting. In: Proceedings of the 18th Pacific-Asia conference on advances in knowledge discovery and data mining PAKDD 2014 Part II, Tainan, pp 234–246

  34. Wang Y, Lin X, Wu L, Zhang W, Zhang Q (2014) Exploiting correlation consensus: towards subspace clustering for multi-modal data. In: Proceedings of the ACM international conference on multimedia, MM ’14. Orlando, pp 981–984

  35. Wang Y, Lin X, Wu L, Zhang W, Zhang Q, Huang X (2015) Robust subspace clustering for multi-view data by exploiting correlation consensus. IEEE Trans Image Process 24(11):3939–3949

    Article  MathSciNet  Google Scholar 

  36. Wang Y, Lin X, Wu L, Zhang W, Zhang Q (2015) LBMCH: learning bridging mapping for cross-modal hashing. In: Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval. Santiago, pp 999–1002

  37. Wang Y, Lin X, Wu L, Zhang W (2015) Effective multi-query expansions: robust landmark retrieval. In: Proceedings of the 23rd annual ACM conference on multimedia conference, MM ’15. Brisbane, pp 79–88

  38. Wang Y, Zhang W, Wu L, Lin X, Fang M, Pan S (2016) Iterative views agreement: an iterative low-rank based structured optimization method to multi-view spectral clustering. In: Proceedings of the twenty-fifth international joint conference on artificial intelligence, IJCAI 2016. New York, pp 2153– 2159

  39. Wang Y, Zhang W, Wu L, Lin X, Zhao X (2017) Unsupervised metric fusion over multiview data by graph random walk-based cross-view diffusion. IEEE Trans Neural Netw Learn Syst 28(1):57– 70

    Article  Google Scholar 

  40. Wang Y, Lin X, Wu L, Zhang W (2017) Effective multi-query expansions: Collaborative deep networks for robust landmark retrieval. IEEE Trans Image Process 26(3):1393–1404

    Article  MathSciNet  Google Scholar 

  41. Wang Y, Wu L (2018) Beyond low-rank representations: Orthogonal clustering basis reconstruction with optimized graph structure for multi-view spectral clustering. Neural Netw 103:1–8

    Article  Google Scholar 

  42. Wang Y, Wu L, Lin X, Gao J (2018) Multiview spectral clustering via structured low-rank matrix factorization. IEEE Trans Neural Networks and Learning Systems

  43. Wu D, Yiu ML, Jensen CS, Cong G (2011) Efficient continuously moving top-k spatial keyword query processing. In: Proceedings of the 27th international conference on data engineering, ICDE 2011. Hannover, pp 541–552

  44. Wu D, Cong G, Jensen CS (2012) A framework for efficient spatial web object retrieval. VLDB J 21(6):797–822

    Article  Google Scholar 

  45. Wu D, Yiu ML, Cong G, Jensen CS (2012) Joint top-k spatial keyword query processing. IEEE Trans Knowl Data Eng 24(10):1889–1903

    Article  Google Scholar 

  46. Wu L, Wang Y, Shepherd J (2013) Efficient image and tag co-ranking: a bregman divergence optimization method. In: ACM multimedia conference, MM ’13. Barcelona, pp 593– 596

  47. Wu L, Wang Y (2017) Robust hashing for multi-view data: jointly learning low-rank kernelized similarity consensus and hash functions. Image Vis Comput 57:58–66

    Article  Google Scholar 

  48. Wu L, Wang Y, Gao J, Li X (2018) Deep adaptive feature embedding with local sample distributions for person re-identification. Pattern Recogn 73:275–288

    Article  Google Scholar 

  49. Wu L, Wang Y, Ge Z, Hu Q, Li X (2018) Structured deep hashing with convolutional neural networks for fast person re-identification. Comput Vis Image Underst 167:63–73

    Article  Google Scholar 

  50. Wu L, Wang Y, Li X, Gao J (2018) Deep attention-based spatially recursive networks for fine-grained visual recognition. IEEE Trans Cybernetics

  51. Wu L, Wang Y, Li X, Gao J (2018) What-and-where to match: Deep spatially multiplicative integration networks for person re-identification. Pattern Recogn 76:727–738

    Article  Google Scholar 

  52. Xiao Z, Qi X (2014) Complementary relevance feedback-based content-based image retrieval. Multimed Tools Appl 73(3):2157–2177

    Article  Google Scholar 

  53. Yao B, Li F, Hadjieleftheriou M, Hou K (2010) Approximate string search in spatial databases. In: Proceedings of the 26th international conference on data engineering, ICDE 2010. Long Beach, pp 545–556

  54. Zhang D, Chee YM, Mondal A, Tung AKH, Kitsuregawa M (2009) Keyword search in spatial databases: Towards searching by document. In: Proceedings of the 25th international conference on data engineering, ICDE 2009. Shanghai, pp 688–699

  55. Zhang D, Ooi BC, Tung AKH (2010) Locating mapped resources in web 2.0. In: Proceedings of the 26th international conference on data engineering, ICDE 2010. Long Beach, pp 521–532

  56. Zhang D, Tan K, Tung AKH (2013) Scalable top-k spatial keyword search. In: Joint 2013 EDBT/ICDT conferences. EDBT ’13 Proceedings. Genoa, pp 359–370

  57. Zhang C, Zhang Y, Zhang W, Lin X (2013) Inverted linear quadtree: Efficient top k spatial keyword search. In: 29th IEEE international conference on data engineering, ICDE 2013. Brisbane, pp 901– 912

  58. Zhang C, Zhang Y, Zhang W, Lin X, Cheema MA, Wang X (2014) Diversified spatial keyword search on road networks. In: Proceedings of the 17th international conference on extending database technology, EDBT 2014. Athens, pp 367–378

  59. Zhang D, Chan C, Tan K (2014) Processing spatial keyword query as a top-k aggregation query. In: The 37th international ACM SIGIR conference on research and development in information retrieval, SIGIR ’14. Gold Coast, pp 355–364

  60. Zhang C, Zhang Y, Zhang W, Lin X (2016) Inverted linear quadtree: efficient top K spatial keyword search. IEEE Trans Knowl Data Eng 28(7):1706–1721

    Article  Google Scholar 

  61. Zhang G, Zeng Z, Zhang S, Zhang Y, Wu W (2017) SIFT matching with CNN evidences for particular object retrieval. Neurocomputing 238:399–409

    Article  Google Scholar 

  62. Zhou Y, Xie X, Wang C, Gong Y, Ma W (2005) Hybrid index structures for location-based web search. In: Proceedings of the 2005 ACM CIKM International conference on information and knowledge management. Bremen, pp 155–162

  63. Zhu L, Shen J, Jin H, Zheng R, Xie L (2015) Content-based visual landmark search via multimodal hypergraph learning. IEEE Trans Cybern 45(12):2756–2769

    Article  Google Scholar 

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (61702560), project (2018JJ3691, 2016JC2011) of Science and Technology Plan of Hunan Province, and the Research and Innovation Project of Central South University Graduate Students(2018zzts177).

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Authors and Affiliations

  1. School of Information Science and Engineering, Central South University, Changsha, People’s Republic of China

    Chengyuan Zhang, Kesheng Cheng, Lei Zhu, Ruipeng Chen, Zuping Zhang & Fang Huang

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  1. Chengyuan Zhang
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  2. Kesheng Cheng
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Correspondence to Zuping Zhang.

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Zhang, C., Cheng, K., Zhu, L. et al. Efficient continuous top-k geo-image search on road network. Multimed Tools Appl 78, 30809–30838 (2019). https://doi.org/10.1007/s11042-018-6633-x

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