Large-Scale EMM Identification with Geometry-constrained Visual Word Correspondence Voting

Abstract

Embedded Media Marker (EMM) identification system allows users to retrieve relevant dynamic media associated with a static paper document via camera phones. The user supplies a query image by capturing an EMM-signified patch of a paper document through a camera phone; the system recognizes the query and in turn retrieves and plays the corresponding media on the phone. Accurate image matching is crucial for positive user experience in this application. To address the challenges posed by large datasets and variations in camera-phone-captured query images, we introduce a novel image matching scheme based on geometrically consistent correspondences. Two matching constraints – “injection” and “approximate global geometric consistency” (AGGC), which are unique in EMM identification, are presented. A hierarchical scheme, combined with two constraining functions, is designed to detect the “injective-AGGC” correspondences between images. A spatial neighborhood search approach is further proposed to address challenging cases with large translational shift. Experimental results on a 100k+ dataset show that our solution achieves high accuracy with low memory and time complexity and outperforms the standard bag-of-words approach.