New filtering approaches for phishing email
Journal of Computer Security - EU-Funded ICT Research on Trust and Security
Efficient logo retrieval through hashing shape context descriptors
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
A polar-based logo representation based on topological and colour features
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
Robust stamps detection and classification by means of general shape analysis
ICCVG'10 Proceedings of the 2010 international conference on Computer vision and graphics: Part I
General shape analysis applied to stamps retrieval from scanned documents
AIMSA'10 Proceedings of the 14th international conference on Artificial intelligence: methodology, systems, and applications
Categorization of camera captured documents based on logo identification
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part II
Stamp detection in scanned documents
Annales UMCS, Informatica
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
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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 detec- tion and extraction in document images that robustly classi- fies and precisely localizes logos using a boosting strategy across multiple image scales. At a coarse scale, a trained Fisher classifier performs initial classification using fea- tures from document context and connected components. Each logo candidate region is further classified at succes- sively 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 met- ric to measure the quality of logo detection using labeled groundtruth. We demonstrate the effectiveness of our ap- proach using a large collection of real-world documents.