Modeling and analyzing the topicality of art images

AbstractThis study demonstrates an improved conceptual foundation to support well-structured analysis of image topicality. First we present a conceptual framework for analyzing image topicality, explicating the layers, the perspectives, and the topical relevance relationships involved in modeling the topicality of art images. We adapt a generic relevance typology to image analysis by extending it with definitions and relationships specific to the visual art domain and integrating it with schemes of image-text relationships that are important for image subject indexing. We then apply the adapted typology to analyze the topical relevance relationships between 11 art images and 768 image tags assigned by art historians and librarians. The original contribution of our work is the topical structure analysis of image tags that allows the viewer to more easily grasp the content, context, and meaning of an image and quickly tune into aspects of interest; it could also guide both the indexer and the searcher to specify image tags/descriptors in a more systematic and precise manner and thus improve the match between the two parties. An additional contribution is systematically examining and integrating the variety of image-text relationships from a relevance perspective. The paper concludes with implications for relational indexing and social tagging.