Ranking Loss for Writer Identification on Music Scores

Type: MA thesis

Status: finished

Date: December 1, 2019 - July 6, 2020

Supervisors: Vincent Christlein, Andreas Maier

Writer identification is a one-shot classification problem that is often performed solely on textual
handwriting as the data is easy to obtain. But also on handwritten music scores promises this task a
significant knowledge gain, especially as old music scores were often copied by hand even though
music engraving is known to exist since the late sixteenth century [1]. As pointed out by [3], Naı̈ve
Bayes Nearest Neighbor (NBNN) classifiers can natively not be used as Convolutional Neural Network
(CNN) activations or final layer of CNN end-to-end training. They proposed a scalable version of
Naive Bayes Non-linear Learning (NBNL) to address this problem. Besides, Mohammed et al. [4]
improved the classifier’s robustness to unbalanced data and added constraints to prevent matching of
irrelevant key points, adapting it specially for the task of writer identification.
Ranking is commonly used in evaluation metrics and it seems greatly desirable for the task of writer
recognition. Through incorporating the ’SoDeep’-layer as proposed by Engilberge et al. [2], we can
learn a loss function for our classification task. This also allows to introduce loss functions, which are
closer to the actual metrics of interest. The focus of this work will be to incorporate a NBNN classifier
into SoDeep.
In this work, a ranking loss layer is incorporated into a deep neural network architecture, allowing a
better classification by the local naive Bayes nearest neighbor approach for writer recognition.
The thesis consists of the following milestones:
• Setting up a writer identification framework.
• Incorporating the ’Normalized Local Naive Bayes Nearest Neighbor’ classifier as proposed
in [4].
• Implementing the sorting layer from the SoDeep-paper [2] for our metrics.
The implementation should be done in Python using Pytorch.

[1] Music engraving. URL:
[2] Martin Engilberge, Louis Chevallier, Patrick Pérez, and Matthieu Cord. SoDeep: a Sorting Deep net to
learn ranking loss surrogates. In Proceedings of the IEEE Conference on Computer Vision and Pattern
Recognition, pages 10792–10801, 2019.
[3] Ilja Kuzborskij, Fabio Maria Carlucci, and Barbara Caputo. When naive bayes nearest neighbors meet
convolutional neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern
Recognition, pages 2100–2109, 2016.[4] Hussein Mohammed, Volker Märgner, Thomas Konidaris, and H Siegfried Stiehl. Normalised Local Naı̈ve
Bayes Nearest-Neighbour Classifier for Offline Writer Identification. In 2017 14th IAPR International
Conference on Document Analysis and Recognition (ICDAR), volume 1, pages 1013–1018. IEEE, 2017.