A Hierarchical Approach to Practical Beverage Package Recognition

In this paper we study the beverage package recognition problem for mobile applications. Unlike products such as books and CDs that are primarily packaged in rigid forms, the beverage labels may be attached on various forms including cans and bottles. Therefore, query images captured by users may have a wide range or variations in appearance. Furthermore, similar visual patterns may appear on distinct beverage packages that belong to the same series. To address these challenges, we propose a fast, hierarchical approach that can be used to effectively recognize a beverage package in real-time. A weighting scheme is introduced to enhance the recognition accuracy rate when the query beverage is among flavor varieties in a series. We examine the development of a practical system that can achieve a fairly good recognition performance (93% accuracy rate using an evaluation set of 120 images) in real-time.

[1]  Michael Isard,et al.  Object retrieval with large vocabularies and fast spatial matching , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Bernd Girod,et al.  Comparison of local feature descriptors for mobile visual search , 2010, 2010 IEEE International Conference on Image Processing.

[3]  Yan Ke,et al.  An efficient parts-based near-duplicate and sub-image retrieval system , 2004, MULTIMEDIA '04.

[4]  Bill Glover,et al.  RFID essentials , 2006 .

[5]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[6]  Roger C. Palmer,et al.  The bar code book : reading, printing, and specification of bar code symbols , 1989 .

[7]  David Nistér,et al.  Scalable Recognition with a Vocabulary Tree , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[8]  Bernd Girod,et al.  CHoG: Compressed histogram of gradients A low bit-rate feature descriptor , 2009, CVPR.

[9]  Bernd Girod,et al.  Rate-efficient, real-time cd cover recognition on a camera-phone , 2008, ACM Multimedia.

[10]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[11]  Yan Ke,et al.  Efficient Near-duplicate Detection and Sub-image Retrieval , 2004 .

[12]  Christopher Hunt,et al.  Notes on the OpenSURF Library , 2009 .

[13]  Oliver Günther,et al.  Multidimensional access methods , 1998, CSUR.

[14]  Bernd Girod,et al.  Mobile product recognition , 2010, ACM Multimedia.

[15]  Hwann-Tzong Chen,et al.  Probing the local-feature space of interest points , 2010, 2010 IEEE International Conference on Image Processing.