Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
A writer identification and verification system
Pattern Recognition Letters
A writer identification and verification system using HMM based recognizers
Pattern Analysis & Applications
A writer identification system for on-line whiteboard data
Pattern Recognition
A Comparative Study of Staff Removal Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Writer Identification in Old Handwritten Music Scores
DAS '08 Proceedings of the 2008 The Eighth IAPR International Workshop on Document Analysis Systems
Writer identification of Arabic handwriting documents using grapheme features
AICCSA '08 Proceedings of the 2008 IEEE/ACS International Conference on Computer Systems and Applications
AICCSA '08 Proceedings of the 2008 IEEE/ACS International Conference on Computer Systems and Applications
Blurred Shape Model for binary and grey-level symbol recognition
Pattern Recognition Letters
On the Use of Textural Features for Writer Identification in Old Handwritten Music Scores
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Fisher Kernels for Handwritten Word-spotting
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
A bag of notes approach to writer identification in old handwritten musical scores
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
Improving the fisher kernel for large-scale image classification
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
A combination of features for symbol-independent writer identification in old music scores
International Journal on Document Analysis and Recognition
Bag of Characters and SOM Clustering for Script Recognition and Writer Identification
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
A Bag-of-Pages Approach to Unordered Multi-page Document Classification
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
A Symbol-Dependent Writer Identification Approach in Old Handwritten Music Scores
ICFHR '10 Proceedings of the 2010 12th International Conference on Frontiers in Handwriting Recognition
Automatic identification of music notations
WEDELMUSIC'02 Proceedings of the Second international conference on Web delivering of music
The ICDAR 2011 Music Scores Competition: Staff Removal and Writer Identification
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
Image categorization using Fisher kernels of non-iid image models
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Assessing the aesthetic quality of photographs using generic image descriptors
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Writer Identification is an important task for the automatic processing of documents. However, the identification of the writer in graphical documents is still challenging. In this work, we adapt the Bag of Visual Words framework to the task of writer identification in handwritten musical scores. A vanilla implementation of this method already performs comparably to the state-of-the-art. Furthermore, we analyze the effect of two improvements of the representation: a Bhattacharyya embedding, which improves the results at virtually no extra cost, and a Fisher Vector representation that very significantly improves the results at the cost of a more complex and costly representation. Experimental evaluation shows results more than 20 points above the state-of-the-art in a new, challenging dataset.