MICC laboratories, Florence, 31th October 2013 (10.15-13.15). Course on Multimedia Databases (DBMM) – laboratory lecture.
- Goal: logo recognition in web images.
- Dataset/testset: find 4 different logos vs 110 images.
- Evaluation metrics: recognition performances will be evaluated in terms of mean Average Precision (mAP).
Instructors: Lamberto Ballan, Lorenzo Seidenari.
Download Software & Dataset 
(* based on VLFeat library by A. Vedaldi)
Final results (ranking): http://goo.gl/o5DCG5
Our paper entitled “Context-Dependent Logo Matching and Recognition” – by H. Sahbi, L. Ballan, G. Serra and A. Del Bimbo – has been accepted for publication in the IEEE Transactions on Image Processing (pdf, link). Part of this work was conducted while me and G. Serra were visiting scholars at Telecom ParisTech (in spring 2010).
We contribute through this paper 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 introduce a detection/recognition procedure and we study 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.
Our paper “Commercials and Trademarks Recognition” has been accepted as book chapter in TV Content Analysis: Techniques and Applications that will be published by CRC Press, Taylor & Francis group, on March 2012.
Book summary: TV content is currently available through various communication channels and devices, including digital TV, mobile TV, and Internet TV. However, with the increase in TV content volume, both its management and consumption become more and more challenging. Thoroughly describing TV program analysis techniques, this book explores the systems, architectures, algorithms, applications, research results, new approaches, and open issues. Leading experts address a wide variety of related subject areas and present a scientifically sound treatment of state-of-the-art techniques for readers interested or involved in TV program analysis.
From April 6 to June 30, 2010, I will be a visiting PhD student at Telecom Paristech in Paris (France). Telecom Paristech (also known as ENST) is one of the most prestigious and selective grandes écoles in France and one of the finest institutions in the field of Telecommunications.
Together with Giuseppe Serra, we will work in the Image Processing and Interpretation (TII) group in the department of Signal and Image Processing (TSI), collaborating with Dr. Hichem Sahbi.
MICC laboratories, Florence, 21st October 2009 (10.30-13.30). Course on Multimedia Databases (DBMM) – laboratory lecture.
- Goal: logo recognition in web images.
- Dataset/testset: find 2 different logos vs 100 images.
- Evaluation metrics: recognition performances will be evaluated in terms of Precision and Recall.
Tutors: Lamberto Ballan, Lorenzo Seidenari.
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(with references) | Download Software & Dataset
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