Automatic image annotation based on an improved nearest neighbor technique with tag semantic extension model
نویسندگان
چکیده
منابع مشابه
Fuzzy Neighbor Voting for Automatic Image Annotation
With quick development of digital images and the availability of imaging tools, massive amounts of images are created. Therefore, efficient management and suitable retrieval, especially by computers, is one of themost challenging fields in image processing. Automatic image annotation (AIA) or refers to attaching words, keywords or comments to an image or to a selected part of it. In this paper,...
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Most of the Nearest Neighbor (NN) based image annotation (or classification) methods cannot achieve satisfactory performance, due to the fact that information loss is inevitable when extracting visual features from images, such as constructing bag of visual words based features. In this paper, we propose a novel NN method based on semantic distance, which improves classification performance by ...
متن کاملfuzzy neighbor voting for automatic image annotation
with quick development of digital images and the availability of imaging tools, massive amounts of images are created. therefore, efficient management and suitable retrieval, especially by computers, is one of themost challenging fields in image processing. automatic image annotation (aia) or refers to attaching words, keywords or comments to an image or to a selected part of it. in this paper,...
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An Improved K-Nearest Neighbor with Crow Search Algorithm for Feature Selection in Text Documents Classification
The Internet provides easy access to a kind of library resources. However, classification of documents from a large amount of data is still an issue and demands time and energy to find certain documents. Classification of similar documents in specific classes of data can reduce the time for searching the required data, particularly text documents. This is further facilitated by using Artificial...
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ژورنال
عنوان ژورنال: Procedia Computer Science
سال: 2021
ISSN: 1877-0509
DOI: 10.1016/j.procs.2021.02.105