%0 Conference Paper %1 4377038 %A Zhu, Guangyu %A Doermann, D. %D 2007 %J Document Analysis and Recognition, 2007. ICDAR 2007. Ninth International Conference on %K Fisher automatic boosting classifier detection document extraction feature groundtruth image images labeled logo processing recognition retrieval strategy %P 864-868 %R 10.1109/ICDAR.2007.4377038 %T Automatic Document Logo Detection %V 2 %X Automatic logo detection and recognition continues to be of great interest to the document retrieval community as it enables effective identification of the source of a document. In this paper, we propose a new approach to logo detection and extraction in document images that robustly classifies and precisely localizes logos using a boosting strategy across multiple image scales. At a coarse scale, a trained Fisher classifier performs initial classification using features from document context and connected components. Each logo candidate region is further classified at successively finer scales by a cascade of simple classifiers, which allows false alarms to be discarded and the detected region to be refined. Our approach is segmentation free and lay-out independent. We define a meaningful evaluation metric to measure the quality of logo detection using labeled groundtruth. We demonstrate the effectiveness of our approach using a large collection of real-world documents.

@inproceedings{4377038, abstract = {Automatic logo detection and recognition continues to be of great interest to the document retrieval community as it enables effective identification of the source of a document. In this paper, we propose a new approach to logo detection and extraction in document images that robustly classifies and precisely localizes logos using a boosting strategy across multiple image scales. At a coarse scale, a trained Fisher classifier performs initial classification using features from document context and connected components. Each logo candidate region is further classified at successively finer scales by a cascade of simple classifiers, which allows false alarms to be discarded and the detected region to be refined. Our approach is segmentation free and lay-out independent. We define a meaningful evaluation metric to measure the quality of logo detection using labeled groundtruth. We demonstrate the effectiveness of our approach using a large collection of real-world documents.}, added-at = {2009-04-23T17:17:00.000+0200}, author = {Zhu, Guangyu and Doermann, D.}, biburl = {https://www.bibsonomy.org/bibtex/268dfe18983916932a5b94683cbbe0326/dieudonnew}, doi = {10.1109/ICDAR.2007.4377038}, interhash = {50018496427a657287df168ba2fbf53b}, intrahash = {68dfe18983916932a5b94683cbbe0326}, issn = {1520-5363}, journal = {Document Analysis and Recognition, 2007. ICDAR 2007. Ninth International Conference on}, keywords = {Fisher automatic boosting classifier detection document extraction feature groundtruth image images labeled logo processing recognition retrieval strategy}, month = {Sept.}, pages = {864-868}, timestamp = {2009-04-23T17:17:00.000+0200}, title = {Automatic Document Logo Detection}, volume = 2, year = 2007 }