high-dimensional visual vocabularies for image retrieval
In this paper we formulate image retrieval by text query as a vector space classification problem. This is achieved by creating a high-dimensional visual vocabulary that represents the image documents in great detail. We show how the representation of these image documents enables the application of well known text retrieval techniques such as Rocchio tf-idf and nave Bayes to the semantic image retrieval problem. We tested these methods on a Corel images subset and achieve state-of-the-art retrieval performance using the proposed methods.
visualization image image retrieval image retrieval paper text query vectorization space classification documentation representation applications semantic method performance semantic image indexing vector space model art imaging