%0 Conference Proceedings
%T Sparse Radial Sampling LBP for Writer Identification
%A A.Nicolaou
%A Andrew Bagdanov
%A Marcus Liwicki
%A Dimosthenis Karatzas
%B 13th International Conference on Document Analysis and Recognition ICDAR2015
%D 2015
%F A.Nicolaou2015
%O DAG; 600.077
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2692), last updated on Tue, 18 Oct 2016 17:56:39 +0200
%X In this paper we present the use of Sparse Radial Sampling Local Binary Patterns, a variant of Local Binary Patterns (LBP) for text-as-texture classification. By adapting and extending the standard LBP operator to the particularities of text we get a generic text-as-texture classification scheme and apply it to writer identification. In experiments on CVL and ICDAR 2013 datasets, the proposed feature-set demonstrates State-Of-the-Art (SOA) performance. Among the SOA, the proposed method is the only one that is based on dense extraction of a single local feature descriptor. This makes it fast and applicable at the earliest stages in a DIA pipeline without the need for segmentation, binarization, or extraction of multiple features.
%U http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7321714
%U http://refbase.cvc.uab.es/files/NBL2015.pdf
%P 716-720