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AutoLabel: Labeling Places from Pictures and Websites

AutoLabel: Labeling Places from Pictures and Websites. Rufeng Meng, Sheng Shen, Romit Roy Choudhury, Srihari Nelakuditi. Localization. Coordinates  Names. Wi-Fi Based Semantic Localization. Wi-Fi Based Semantic Localization. Core Problem. ?. AP Name Mining. AP Name Mining.

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AutoLabel: Labeling Places from Pictures and Websites

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  1. AutoLabel: Labeling Places from Pictures and Websites Rufeng Meng, Sheng Shen, Romit Roy Choudhury, Srihari Nelakuditi

  2. Localization Coordinates  Names

  3. Wi-Fi Based Semantic Localization

  4. Wi-Fi Based Semantic Localization

  5. Core Problem ?

  6. AP Name Mining AP NameMining Best Buyon Street Macy’sin Mall

  7. Opportunity: Stores have two avatars

  8. Opportunity: Stores have two avatars

  9. Mapping AP Vector to Store Name OCR AutoLabelMatching In-store Text with Website Text Matching In-store Textwith Website Text Text From Candidate Webpages Best Buy Whole Foods Panera Starbucks

  10. Candidate Store Names CrowdSourcing m n

  11. CrowdSourcing m n Candidate Store Names

  12. CrowdSourcing OCRed In-store Text 1 Text-based Matching 1 Candidate Store Names

  13. Text Extraction Above Eye-level • Store Text • Text OCRed from In-store Pictures • Web Text • Candidate Store Names • Meta Keywords in Web Pages • Text in Menu/Category Items Eye-level Below Eye-level

  14. Noun/Proper Name Extraction Filter Weight Assignment Bag of Words Similarity Calculation Labeling Noun/Proper Name Extraction Filter Weight Assignment Bag of Words • Weight assignment: TF-IDF Text Matching Store Text Web Text

  15. <AP6, AP7, AP8> <AP1, AP2, AP5> <AP2, AP3, AP4> Cluster (Best Buy) (Dollar Tree) (Starbucks) (Panera Bread) Webs of Candidate Stores AutoLabel In-store Textvs. Website Text

  16. Simultaneous Clustering and Labeling • Two images put in different clusters if no common AP • Generate potential clusterings of images • Similarity of clustering = sum of similarities of its clusters • Pick the clustering with highest similarity • Label the AP vectors as per the best clustering

  17. Evaluation

  18. Data Collection • 40 Stores • 18 from shopping mall @Champaign, IL • 10 from one street area @Champaign, IL • 6 from other places @Champaign, IL • 6 from shopping mall @San Jose, CA • Number of Pics: • 6 ~ 217 pics/store(with readable text)

  19. Text Matching without Store Names Accuracy: 80%

  20. Text Matching with Store Names Accuracy: 87%

  21. Text Matching without Store Names (Mall) 16/18 Correctly Matched

  22. Text Matching with Store Names (Mall) 17/18 Correctly Matched

  23. Text Matching without Store Names (Street) 10/10 Correctly Matched

  24. Accuracy with Varying #Pics/Store

  25. Accuracy with Varying #Pics/Store

  26. Accuracy with Varying #Stores/Area Mean Accuracy: • 5-store: 100% • 20-store: 90%

  27. Performance of Place Recognition with AP Vector– Store Name Mapping

  28. Simultaneous Clustering and Labeling

  29. Related Work • Visual business recognition • Image based matching of the exterior of store • Human assisted positioning using textual signs • Requires user intervention to localize self • Correlate crowdsourced pictures with social network posts • Stronger correlation between store and its website

  30. Limitations & Future Work • WiFi AP tagged in-store pictures • Reduce uncertainty even with a few pictures • Common decorations besides text • Image clustering • Spatial relationship between stores • Color/Theme + AP name mining • Beyond semantic localization • Correlate website and in-store shopping behavior

  31. Summary of AutoLabel

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