How to Learn Picture Languages

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F19%3A10424549" target="_blank" >RIV/00216208:11320/19:10424549 - isvavai.cz</a>

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=ELNCPYr0B7" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=ELNCPYr0B7</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

How to Learn Picture Languages

Popis výsledku v původním jazyce

Analysis of sentences in a natural language is often based on similar methods as analysis of formal languages. Analogically, analysis of pictures could be based on analysis of formal picture languages. However, the field of formal picture languages is not developed enough for this purpose. This paper presents several models of automata accepting two-dimensional languages and outlines their learning capabilities. Further, it examines the possibility of transforming a two-dimensional language into a one-dimensional language and applying machine learning techniques in a single dimension. In this paper, we propose a new representation for formal picture languages consisting of two components - a picture-to-string function and a string language. The function rewrites any two-dimensional picture into a string. A picture language is then the set of all pictures that the function maps into the given string language. Using this representation, picture languages can be learned by applying methods of grammatical inference for string languages.

Název v anglickém jazyce

How to Learn Picture Languages

Popis výsledku anglicky

Analysis of sentences in a natural language is often based on similar methods as analysis of formal languages. Analogically, analysis of pictures could be based on analysis of formal picture languages. However, the field of formal picture languages is not developed enough for this purpose. This paper presents several models of automata accepting two-dimensional languages and outlines their learning capabilities. Further, it examines the possibility of transforming a two-dimensional language into a one-dimensional language and applying machine learning techniques in a single dimension. In this paper, we propose a new representation for formal picture languages consisting of two components - a picture-to-string function and a string language. The function rewrites any two-dimensional picture into a string. A picture language is then the set of all pictures that the function maps into the given string language. Using this representation, picture languages can be learned by applying methods of grammatical inference for string languages.

Druh

J_ost - Ostatní články v recenzovaných periodicích

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Research in Computing Science

ISSN

1870-4069

e-ISSN

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Svazek periodika

148

Číslo periodika v rámci svazku

11

Stát vydavatele periodika

MX - Spojené státy mexické

Počet stran výsledku

12

Strana od-do

115-126

Kód UT WoS článku

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EID výsledku v databázi Scopus

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