A Context-Dependent Kernel for Logo Recognition

By , March 22, 2013

Collaborators: Hichem Sahbi*, Giuseppe Serra, Alberto Del Bimbo

A Context-Dependent Kernel for Logo RecognitionWe contributed through this work to the design of a novel variational framework able to match and recognize multiple instances of multiple reference logos in image archives. Reference logos as well as test images, are seen as constellations of local features (interest points, regions, etc.) and matched by minimizing an energy function mixing (i) a fidelity term that measures the quality of feature matching (ii) a neighborhood criterion which captures feature co-occurrence/geometry and (iii) a regularization term that controls the smoothness of the matching solution.

We also introduced a detection/recognition procedure and we studied its theoretical consistency. We show the validity of our method through extensive experiments on the novel challenging MICC-Logos dataset overtaking, by 20%, baseline as well as state-of-the-art matching/recognition procedures. We present also results on another public dataset, the FlickrLogos-27 image collection, to demonstrate the generality of our method.

* Part of this work was conducted while me and Giuseppe Serra were visiting scholars at Telecom ParisTech (in spring 2010) under the supervision of Hichem Sahbi.

Related publications:

[bibtex file=ballan.bib key=tip13][bibtex file=others.bib key=enst10]


  • MICC-Logos: this dataset is composed by 720 images downloaded from the web in January 2010; it contains 13 logo classes each one represented with 15-87 real world pictures.

If you use this dataset, please cite the paper: H. Sahbi, L. Ballan, G. Serra, and A. Del Bimbo, “Context-Dependent Logo Matching and Recognition”, IEEE Transactions on Image Processing, vol. 22, iss. 3, pp. 1018-1031, 2013.

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