scholarly journals Chinese-Russian Visual Dictionary with Transcription / N. N. Voropaev (comp.); Ma Tianyu (ed.). Moscow: AST Publ., 2019. 319 p.: Opinion Paper

2021 ◽  
pp. 2655-2656
Author(s):  
Andrey Ivanovich Izotov ◽  
Keyword(s):  
Visual Dictionary

Data-driven two-layer visual dictionary structure learning

2019 ◽  
Vol 28 (02) ◽  
pp. 1 ◽  
Author(s):  
Xiangchun Yu ◽  
Zhezhou Yu ◽  
Lei Wu ◽  
Wei Pang ◽  
Chenghua Lin
Keyword(s):  
Structure Learning ◽  
Data Driven ◽  
Visual Dictionary ◽  
Dictionary Structure

Multiple Features Image Fusion Based on Visual Dictionary

2014 ◽  
Vol 889-890 ◽  
pp. 1111-1114
Author(s):  
Yu Ting
Keyword(s):  
Semantic Processing ◽  
Image Annotation ◽  
Visual Feature ◽  
Web Based ◽  
Automatic Tagging ◽  
Processing Module ◽  
Heavy Work ◽  
Text Information ◽  
Visual Dictionary ◽  
High Level

It has been heavy work that to find the related pictures form Internet without annotation. Therefore, the automatic image annotation was extremely important in image retrieval. The traditional method were translated image visual feature into keywords simply, but it ignored the image similarity problem between the low-level visual features and high-level semantic. That is image "gap" problem, so image annotation was very lower. This paper puts forward a classification of web based image content automatic tagging mixing technology, the first it will map visual feature of image to one or more rough images, then we will preprocess the web page text information, finally we select some keywords similarity as image annotation by using similar semantic processing module. So it realizes the image and text combining the automatic annotation and it achieve high precision of image annotation.

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