Applying uncertain frequent pattern mining to improve ranking of retrieved images

Immense increase in digital images demands an effective image retrieval system. In text based image retrieval systems, textual keywords are used for indexing. The query keywords are matched with the keywords associated with images to perform image retrieval task. Users usually demand higher proportion of the query keywords in the retrieved images than the other undesired keywords. Existing image retrieval systems retrieve images that do not contain the query keywords either in equal or higher proportion than other keywords. This paper proposes a new image retrieval system that applies fuzzy ontology and uncertain frequent pattern mining for image retrieval to resolve this issue. Image content is represented in terms of concepts and categories. Fuzzy ontology is constructed by utilizing the concepts and categories associated with the images. Uncertain frequent pattern mining is then applied on the association that exists among the concepts in images. These patterns assist in retrieving images, which contain the required query keywords in high proportion than other keywords. The ranking of retrieved images is evaluated with two different measures, i.e., mean and variance and difference at various thresholds of concepts’ occurrences in images. Experimental results show that the proposed image retrieval system performs better than existing ontology based retrieval systems.