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See-To-Retrieve: Efficient Processing of Spatio-Visual Keyword Queries

See-To-Retrieve: Efficient Processing of Spatio-Visual Keyword Queries. Chao Zhang 、 Lidan Shou 、 Ke Chen 、 Gang Chen College of Computer Science Zhejiang University, China. ACM SIGIR 2012. Outline. Spatio-Visual Keyword Visibility Analysis TF-IDF model

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See-To-Retrieve: Efficient Processing of Spatio-Visual Keyword Queries

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  1. See-To-Retrieve: Efficient Processing of Spatio-Visual Keyword Queries Chao Zhang、Lidan Shou、 Ke Chen、Gang Chen College of Computer Science Zhejiang University, China ACM SIGIR 2012

  2. Outline • Spatio-Visual Keyword • Visibility Analysis • TF-IDF model • Efficient process SVK queries • Experiment

  3. Spatio-Visual Keyword • searches for introductory information about a distant grand church • within her eyesight.

  4. Goals visually conspicuous semantically relevant physicalspace document space WYRIWYS (What-You-Retrieve-Is-What-You-See)

  5. Visibility Analysis • determine the visibility of each object • 1.utilizes horizontal projection of objects to determine occlusion

  6. Visibility Analysis 2. Cumulative face visibility • Conspicuous: large and close to the observer : grid size : angle between and :Euclidian distance between and

  7. Visibility Analysis 3. Sum up the visibilities of all the small grids

  8. TF-IDF model o: spatial Web objects

  9. Efficient process SVK queries • Baseline solution

  10. R-tree • 空間索引結構 台北市 新北市 桃園縣 • 優點:經由R-tree空間資料庫索引結構可以精確地抓取所需範圍內的空 • 間資料，不會有資料多餘抓取的問題

  11. Retrieve potential occluders 1.Same angular range 2. Near than the object q

  12. Problemof Baseline solution • It is highly redundant to retrieve potential occluders • repeatedly. • Separation of indexes fails to combine spatial and textual • features of objects and incurs unnecessary overhead.

  13. Efficient process SVK queries • Construct Complete Occlusion-Map (COM) • Retrieve the top-k relevant objects incrementally

  14. IR-tree

  15. MINDIST Y (t1,t2) R (s1,s2) P P: query point R: rectangle n: dimension X

  16. Complete Occlusion-Map

  17. Example - prune node 1. The query point q stays outside N’s MBR 2. N’s MBR is completely occluded by current COM

  18. Retrieve the top-k relevant objects incrementally

  19. Ranking score upper bound

  20. Discussion • The cost of Algorithm 2 and 3 Algorithm 2 is related to the environment around the query point q. Ex. dense area The cost of Algorithm 3 is small, because the upper-bound for IR-tree node is optimal.

  21. Experiment • Data set: • 1.street objects in Los Angeles (contains 131,461 MBRs) • 2.Gowalla (consists of 28,867 Web documents)

  22. Efficiency Study

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