VISUAL CONCEPT LEARNING SYSTEM BASED ON LEXICAL ELEMENTS AND FEATURE KEY POINTS CONJUNCTION
Subject of Research. The paper deals withthe process of visual concept building based on two unlabeled sources of information (visual and textual). Method. Visual concept-based learning is carried out with image patterns and lexical elements simultaneous conjunction. Concept-based learning consists...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Saint Petersburg National Research University of Information Technologies, Mechanics and Optics (ITMO University)
2016-07-01
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| Series: | Naučno-tehničeskij Vestnik Informacionnyh Tehnologij, Mehaniki i Optiki |
| Subjects: | |
| Online Access: | http://ntv.ifmo.ru/file/article/15815.pdf |
| _version_ | 1818421338441777152 |
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| author | V. I. Filatov A. S. Potapov |
| author_facet | V. I. Filatov A. S. Potapov |
| author_sort | V. I. Filatov |
| collection | DOAJ |
| description | Subject of Research. The paper deals withthe process of visual concept building based on two unlabeled sources of information (visual and textual). Method. Visual concept-based learning is carried out with image patterns and lexical elements simultaneous conjunction. Concept-based learning consists of two basic stages: early learning acquisition (primary learning) and lexical-semantic learning (secondary learning). In early learning acquisition stage the visual concept dictionary is created providing background for the next stage. The lexical-semantic learning makes two sources timeline analysis and extracts features in both information channels. Feature vectors are formed by extraction of separated information units in both channels. Mutual information between two sources describes visual concepts building criteria. Main Results. Visual concept-based learning system has been developed; it uses video data with subtitles. The results of research have shown principal ability of visual concepts building by our system. Practical Relevance.Recommended application area of described system is an object detection, image retrieval and automatic building of visual concept-based data tasks. |
| first_indexed | 2024-12-14T13:08:46Z |
| format | Article |
| id | doaj.art-feb23d4ee7ab427790821a45eef2941f |
| institution | Directory Open Access Journal |
| issn | 2226-1494 2500-0373 |
| language | English |
| last_indexed | 2024-12-14T13:08:46Z |
| publishDate | 2016-07-01 |
| publisher | Saint Petersburg National Research University of Information Technologies, Mechanics and Optics (ITMO University) |
| record_format | Article |
| series | Naučno-tehničeskij Vestnik Informacionnyh Tehnologij, Mehaniki i Optiki |
| spelling | doaj.art-feb23d4ee7ab427790821a45eef2941f2022-12-21T23:00:14ZengSaint Petersburg National Research University of Information Technologies, Mechanics and Optics (ITMO University)Naučno-tehničeskij Vestnik Informacionnyh Tehnologij, Mehaniki i Optiki2226-14942500-03732016-07-0116468969610.17586/2226-1494-2016-16-4-689-696VISUAL CONCEPT LEARNING SYSTEM BASED ON LEXICAL ELEMENTS AND FEATURE KEY POINTS CONJUNCTIONV. I. FilatovA. S. PotapovSubject of Research. The paper deals withthe process of visual concept building based on two unlabeled sources of information (visual and textual). Method. Visual concept-based learning is carried out with image patterns and lexical elements simultaneous conjunction. Concept-based learning consists of two basic stages: early learning acquisition (primary learning) and lexical-semantic learning (secondary learning). In early learning acquisition stage the visual concept dictionary is created providing background for the next stage. The lexical-semantic learning makes two sources timeline analysis and extracts features in both information channels. Feature vectors are formed by extraction of separated information units in both channels. Mutual information between two sources describes visual concepts building criteria. Main Results. Visual concept-based learning system has been developed; it uses video data with subtitles. The results of research have shown principal ability of visual concepts building by our system. Practical Relevance.Recommended application area of described system is an object detection, image retrieval and automatic building of visual concept-based data tasks.http://ntv.ifmo.ru/file/article/15815.pdfconcept learningvisual conceptsscene understandingfeature key pointsdescriptorsmachine learning |
| spellingShingle | V. I. Filatov A. S. Potapov VISUAL CONCEPT LEARNING SYSTEM BASED ON LEXICAL ELEMENTS AND FEATURE KEY POINTS CONJUNCTION Naučno-tehničeskij Vestnik Informacionnyh Tehnologij, Mehaniki i Optiki concept learning visual concepts scene understanding feature key points descriptors machine learning |
| title | VISUAL CONCEPT LEARNING SYSTEM BASED ON LEXICAL ELEMENTS AND FEATURE KEY POINTS CONJUNCTION |
| title_full | VISUAL CONCEPT LEARNING SYSTEM BASED ON LEXICAL ELEMENTS AND FEATURE KEY POINTS CONJUNCTION |
| title_fullStr | VISUAL CONCEPT LEARNING SYSTEM BASED ON LEXICAL ELEMENTS AND FEATURE KEY POINTS CONJUNCTION |
| title_full_unstemmed | VISUAL CONCEPT LEARNING SYSTEM BASED ON LEXICAL ELEMENTS AND FEATURE KEY POINTS CONJUNCTION |
| title_short | VISUAL CONCEPT LEARNING SYSTEM BASED ON LEXICAL ELEMENTS AND FEATURE KEY POINTS CONJUNCTION |
| title_sort | visual concept learning system based on lexical elements and feature key points conjunction |
| topic | concept learning visual concepts scene understanding feature key points descriptors machine learning |
| url | http://ntv.ifmo.ru/file/article/15815.pdf |
| work_keys_str_mv | AT vifilatov visualconceptlearningsystembasedonlexicalelementsandfeaturekeypointsconjunction AT aspotapov visualconceptlearningsystembasedonlexicalelementsandfeaturekeypointsconjunction |