Abstract
A vast quantity of art in existence today is inaccessible to individuals. If people want to know the different types of art that exist, how individual works are connected, and how works of art are interpreted and discussed in the context of other works, they must utilize means other than simply viewing the art. Therefore, this paper proposes a language to analyze, describe, and explore collections of visual art (LadeCA). LadeCA combines human interpretation and automatic analyses of images, allowing users to assess collections of visual art without viewing every image in them. This paper focuses on the lexical base of LadeCA. It also outlines how collections of visual art can be analyzed, described, and explored using a LadeCA vocabulary. Additionally, the relationship between LadeCA and indexing systems, such as ICONCLASS or AAT, is demonstrated, and ways in which LadeCA and indexing systems can complement each other are highlighted.EhrhRXFrQ66ZUTK35jhESQVideo abstract.
Highlights
The number of digital images is constantly growing, and there are currently billions of images available
How well can the target group distinguish LadeCA words, and how strong is the relationship between the distinctness of the words and calculated relatedness R?
This paper introduced the lexical base of LadeCA, a language that allows users to analyze, describe, and explore visual art
Summary
The number of digital images is constantly growing, and there are currently billions of images available. The classifiers and associated comparison functions are generated automatically by LadeCA during the word formation process (Fig. 5). They are expensive to generate because they require expert knowledge and manual data input; any given type of metadata must have the same meaning and structure for all images used (from various databases); elaborate comparison functions require real (art) world knowledge for development.
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