Christlein V, Bernecker D, Maier A, Angelopoulou E (2015)
Publication Type: Conference contribution
Publication year: 2015
Publisher: Springer International Publishing
Edited Volumes: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Series: Lecture Notes in Computer Science
City/Town: Berlin
Book Volume: 9358
Pages Range: 540-552
Conference Proceedings Title: Pattern Recognition
Event location: Aachen
ISBN: 978-3-319-24946-9
DOI: 10.1007/978-3-319-24947-6_45
Convolutional neural networks (CNNs) have recently become the state-of-the-art tool for large-scale image classification. In this work we propose the use of activation features from CNNs as local descriptors for writer identification. A global descriptor is then formed by means of GMM supervector encoding, which is further improved by normalization with the KL-Kernel. We evaluate our method on two publicly available datasets: the ICDAR 2013 benchmark database and the CVL dataset. While we perform comparably to the state of the art on CVL, our proposed method yields about 0.21 absolute improvement in terms of mAP on the challenging bilingual ICDAR dataset.
APA:
Christlein, V., Bernecker, D., Maier, A., & Angelopoulou, E. (2015). Offline Writer Identification Using Convolutional Neural Network Activation Features. In Pattern Recognition (pp. 540-552). Aachen: Berlin: Springer International Publishing.
MLA:
Christlein, Vincent, et al. "Offline Writer Identification Using Convolutional Neural Network Activation Features." Proceedings of the German Conference on Pattern Recognition, Aachen Berlin: Springer International Publishing, 2015. 540-552.
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