Image retrieval: Modelling keywords via low-level features
Journal
Electronic Letters on Computer Vision and Image Analysis,
Date Issued
2015
Author(s)
DOI
10.5565/rev/elcvia.725
10.5565/rev/elcvia.725
Abstract
With the advent of cheap digital recording and storage devices and the rapidly increasing popularity of online social networks that make extended use of visual information, like Facebook and Instagram, image retrieval regained great attention among the researchers in the areas of image indexing and retrieval. Image retrieval methods are mainly falling into content-based and text-based frameworks. Although content-based image retrieval has attracted large amount of research interest, the difficulties in querying by an example propel ultimate users towards text queries. Searching by text queries yields more effective and accurate results that meet the needs of the users while at the same time preserves their familiarity with the way traditional search engines operate. However, text-based image retrieval requires images to be annotated i.e. they are related to text information. Much effort has been invested on automatic image annotation methods [1], since the manual assignment of keywords (which is necessary for text-based image retrieval) is a time consuming and labour intensive procedure [2].
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