Image recommendation is an important feature of search engine, as tremendous amount of images are available online. It is necessary to retrieve relevant images to meet the user's requirement. In this paper, we present an algorithm image recommendation with absorbing Markov chain (IRAbMC) to retrieve relevant images for a user's input query. Images are ranked by calculating keyword relevance probability between annotated keywords from log and keywords of user input query. Keyword relevance is computed using absorbing Markov chain. Images are reranked using image visual features. Experimental results show that the IRAbMC algorithm outperforms Markovian semantic indexing (MSI) method with improved relevance score of retrieved ranked images.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.