Feature Bundling for Object Retrieval / Logo Recognition
Computer vision and image retrieval are inherently linked with methods that describe visual information and by this the spatial layout of image intensities and colors. Analogous to sentences, where the position of single words is subject to grammar rules, the position of visual structures in images is not arbitrary but depends on the depicted content. In other words, the spatial distribution of individual visual features does have a semantic meaning.
In this project we explore feature bundling techniques suitable for object retrieval and logo recognition. Discriminative visual signatures that include both visual and spatial information are formed by bundling local features within a combined representation. Each bundle is stored in a hash-based index and associated with the underlying object class. Multi-class recognition of objects in unknown test images is then performed by testing if bundles of the test image are contained in this index.
For more information please contact Stefan Romberg.
- Stefan Romberg, Lluis Garcia Pueyo, Rainer Lienhart, Roelof van Zwol. Scalable Logo Recognition in Real-World Images. ACM International Conference on Multimedia Retrieval 2011 (ICMR11), Trento, April 2011.
Also Technical Report 2011-04, University of Augsburg, Institute of Computer Science, March 2011 [ PDF ] [ Slides ] [ Dataset]