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Semantic Robot Vision Challenge

Semantic Robot Vision Challenge. Ashutosh Kulkarni Shashank Senapaty Priyanka Singh March 17, 2008. Problem Statement.

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Semantic Robot Vision Challenge

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  1. Semantic Robot Vision Challenge Ashutosh Kulkarni Shashank Senapaty Priyanka Singh March 17, 2008

  2. Problem Statement • Semantic Robot Vision Challenge (SRVC): Research competition designed to push the state of the art in image understanding and automatic acquisition of knowledge from large unstructured databases of images (such as those generally found on the web). • Use web to find image examples of objects • Identify these objects in a room environment Ashutosh Kulkarni • Shashank Senapaty • Priyanka Singh

  3. Approach • Standard learning appearance models do not work • Unreliable training images CD “Hey Eugene” by Pink Martini • Solution: Rely on matching local features Ashutosh Kulkarni • Shashank Senapaty • Priyanka Singh

  4. Basic Algorithm • Matched SIFT features between training and test images Ashutosh Kulkarni • Shashank Senapaty • Priyanka Singh

  5. Bad (!) training images CD “Hey Eugene” by Pink Martini2.jpg Ashutosh Kulkarni • Shashank Senapaty • Priyanka Singh

  6. Ranking (Google image search) Ashutosh Kulkarni • Shashank Senapaty • Priyanka Singh

  7. Ranking (within group similarity) Ashutosh Kulkarni • Shashank Senapaty • Priyanka Singh

  8. Problem with similarity based ranking Needed: DVD of Shrek 1 Ashutosh Kulkarni • Shashank Senapaty • Priyanka Singh

  9. Elimination and Sub-classification • Weaker notion of a “good” training image – An image which matches with at least one other image from the class. • Remove images that do not match with any other image in the class Ashutosh Kulkarni • Shashank Senapaty • Priyanka Singh

  10. New Ranking results Good Images -- Class 1: Good Images --Class 2: Eliminated images: Ashutosh Kulkarni • Shashank Senapaty • Priyanka Singh

  11. But everything’s Harry Potter! False Positives: Ashutosh Kulkarni • Shashank Senapaty • Priyanka Singh

  12. RANSAC • Throw away outliers • Matching coefficient: Number of inliers / Total number of SIFT matches Ashutosh Kulkarni • Shashank Senapaty • Priyanka Singh

  13. Results after RANSAC Ashutosh Kulkarni • Shashank Senapaty • Priyanka Singh

  14. References: • SRVC : http://www.semantic-robot-vision-challenge.org/ • Scott Helmer, David Meger, Per-Erik Forss´en, Tristram Southey, Sancho McCann, Pooyan Fazli, James J. Little, David G. Lowe. The UBC Semantic Robot Vision System. Webpage: http://www.cs.ubc.ca/~perfo/abstracts/hmfsfll07.html • Scott Helmer, David Meger, Per-Erik Forss´en, Sancho McCann, Tristram Southey,Matthew Baumann, Kevin Lai, Bruce Dow, James J. Little, David G. Lowe. Curious George: The UBC Semantic Robot Vision System. Webpage: http://www.cs.ubc.ca/~perfo/abstracts/hmfmsbldll07.html • Li-Jia Li, Gang Wang and Li Fei-Fei. OPTIMOL: automatic Object Picture collecTion via Incremental Model Learning. CVPR 2007. [PDF] • SIFT implementation: http://web.engr.oregonstate.edu/~hess/index.html • Web crawling resources: http://search.cpan.org/%7Egrousse/WWW-Google-Images-0.6.4/lib/WWW/Google/Images.pm Ashutosh Kulkarni • Shashank Senapaty • Priyanka Singh

  15. Questions? Ashutosh Kulkarni • Shashank Senapaty • Priyanka Singh

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