Optimization for dynamic inverted index maintenance
SIGIR '90 Proceedings of the 13th annual international ACM SIGIR conference on Research and development in information retrieval
The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Finding approximate matches in large lexicons
Software—Practice & Experience
Document Image Decoding Using Markov Source Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Document image similarity and equivalence detection
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Document Image Recognition Based on Template Matching of Component Block Projections
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
ICAT '07 Proceedings of the 17th International Conference on Artificial Reality and Telexistence
HOTPAPER demonstration: multimedia interaction with paper using mobile phones
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Toward Massive Scalability in Image Matching
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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We present a method that addresses image matching from partial blurry images by casting it as a problem of text retrieval. This allows us to leverage existing text document retrieval techniques and achieve efficiency and scalability similar to text search applications. As an initial application, we present a document image matching system in which the user supplies a query image of a small patch of a paper document taken with a cell phone camera, and the system returns a label identifying the original electronic document if found in a previously indexed collection. We have implemented our method in a client server architecture. Feature computation on a mobile client is done in under 100ms, while end-to-end document recognition on a collection of more than 4300 pages requires approximately 500ms per image. Approximately 170ms is connection time and thus subject to network speed variations. We conclude presenting scalability results on a collection of nearly 500,000 documents.