untitled
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<identifier>oai:HAL:hal-01165881v1</identifier>
<datestamp>2015-06-22</datestamp>
<setSpec>type:COMM</setSpec>
<setSpec>subject:info</setSpec>
<setSpec>collection:UNIV-AG</setSpec>
<setSpec>collection:IGS2015</setSpec>
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<metadata><dc>
<publisher>HAL CCSD</publisher>
<title lang=en>Training-and Segmentation-Free Intuitive Writer Identification with Task-Adapted Interest Points</title>
<creator>Garz, Angelika</creator>
<creator>Würsch, Marcel</creator>
<creator>Ingold, Rolf</creator>
<contributor>Albert-Ludwigs-Universität Freiburg</contributor>
<description>International audience</description>
<source>17th Biennial Conference of the International Graphonomics Society</source>
<coverage>Pointe-à-Pitre, Guadeloupe</coverage>
<contributor>International Graphonomics Society (IGS)</contributor>
<contributor>Université des Antilles (UA)</contributor>
<contributor>Céline Rémi</contributor>
<contributor>Lionel Prévost</contributor>
<contributor>Eric Anquetil</contributor>
<identifier>hal-01165881</identifier>
<identifier>https://hal.univ-antilles.fr/hal-01165881</identifier>
<identifier>https://hal.univ-antilles.fr/hal-01165881/document</identifier>
<identifier>https://hal.univ-antilles.fr/hal-01165881/file/IGS_2015_submission_15.pdf</identifier>
<source>https://hal.univ-antilles.fr/hal-01165881</source>
<source>Céline Rémi; Lionel Prévost; Eric Anquetil. 17th Biennial Conference of the International Graphonomics Society, Jun 2015, Pointe-à-Pitre, Guadeloupe. 2015, Drawing, Handwriting Processing Analysis: New Advances and Challenges</source>
<language>en</language>
<subject lang=en>Interest Points (IP)</subject>
<subject lang=en>Writer Identification (WI)</subject>
<subject>[INFO] Computer Science [cs]</subject>
<type>info:eu-repo/semantics/conferenceObject</type>
<type>Conference papers</type>
<description lang=en>Identifying the writer of a document establishes its authenticity or authorship and has several applications, notably in forensic and historical document analysis. Previous research has shown the potential of Interest Points (IP) for writer identification, but existing methods require segmentation or training. This paper evaluates the performance of intuitive features computed directly from IP properties rather than extracting descriptors at their locations; allowing for a training-free approach. Secondly, we show that adapting detectors to the specific task of writer identification is not only vital for performance but also allows for segmentation-free approaches. Experiments on widely-used datasets show the potential of the method applied self-contained and when combined with existing methods. Limitations of our method relate to the amount of data needed in order to obtain reliable models.</description>
<date>2015-06-21</date>
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