The problem of polysemy in keyword-based image search arises mainly from the inherent ambiguity in user queries. We propose a latent model based approach that resolves user search ambiguity by allowing sense specific diversity in search results. Given a query keyword and the images retrieved by issuing the query to an image search engine, we first learn a latent visual sense model of these polysemous images. Next, we use Wikipedia to disambiguate the word sense of the original query, and issue these Wiki-senses as new queries to retrieve sense specific images. A sense-specific image classifier is then learnt by combining information from the latent visual sense model, and used to cluster and re-rank the polysemous images from the original query keyword into its specific senses. Results on a ground truth of 17K image set returned by 10 keyword searches and their 62 word senses provides empirical indications that our method can improve upon existing keyword based search engines. Our method learns the visual word sense models in a totally unsupervised manner, effectively filters out irrelevant images, and is able to mine the long tail of image search.
Another approach, proposed by REF , uses Wikipedia to find the senses of a word.
A Latent Model for Visual Disambiguation of Keyword-based Image Search
BMVC
Computer Science