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<identifier>oai:HAL:hal-01166497v1</identifier>
<datestamp>2015-06-23</datestamp>
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<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>Online Sketch Recognition with Incremental Fuzzy Models</title>
<creator>Tencer, Lukas</creator>
<creator>Režnáková, Marta</creator>
<creator>Cheriet, Mohamed</creator>
<contributor>Département de génie de la production automatisée ; Ecole de Technologie Supérieure [Montréal] (ETS)</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-01166497</identifier>
<identifier>https://hal.univ-antilles.fr/hal-01166497</identifier>
<identifier>https://hal.univ-antilles.fr/hal-01166497/document</identifier>
<identifier>https://hal.univ-antilles.fr/hal-01166497/file/IGS_2015_submission_37.pdf</identifier>
<source>https://hal.univ-antilles.fr/hal-01166497</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>handwritten sketches</subject>
<subject lang=en>SIFT-like</subject>
<subject lang=en>fuzzy-neural networks</subject>
<subject>[INFO] Computer Science [cs]</subject>
<type>info:eu-repo/semantics/conferenceObject</type>
<type>Conference papers</type>
<description lang=en>In this paper, we present a novel method for recognition of handwritten sketches. Unlike previous approaches, we focus on online retrieval and ability to build our model incrementally, thus we do not need to know all the data in advance and we can achieve very good recognition results after as few as 15 samples. The method is composed of two main parts: feature representation and learning and recognition. In feature representation part, we utilize SIFT-like feature descriptors in combination with soft response Bag-of-Words techniques. Descriptors are extracted locally using our novel sketch-specific sampling strategy and for support regions we follow patch-based approach. For learning and recognition, we use a novel technique based on fuzzy-neural networks, which has shown good performance in incremental learning. The experiments on state-of-the-art benchmarks have shown promising results.</description>
<date>2015-06-21</date>
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