Encoding CNN Activations for Writer Recognition

被引：40

作者：

Christlein, Vincent ^{[1
]}

Maier, Andreas ^{[1
]}

机构：

[1] Friedrich Alexander Univ Erlangen Nurnberg, Pattern Recognit Lab, D-91058 Erlangen, Germany

来源：

2018 13TH IAPR INTERNATIONAL WORKSHOP ON DOCUMENT ANALYSIS SYSTEMS (DAS) | 2018年

关键词：

writer identification; writer retrieval; deep learning; document analysis; IDENTIFICATION; RETRIEVAL;

D O I：

10.1109/DAS.2018.9

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The encoding of local features is an essential part for writer identification and writer retrieval. While CNN activations have already been used as local features in related works, the encoding of these features has attracted little attention so far. In this work, we compare the established VLAD encoding with triangulation embedding. We further investigate generalized max pooling as an alternative to sum pooling and the impact of decorrelation and Exemplar SVMs. With these techniques, we set new standards on two publicly available datasets (ICDAR13, KHATT).

引用

页码：169 / 174

页数：6

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