Semantic Image Retrieval Using Region Based Inverted File

Image data is as common as textual data in this digital world. There is an urgent demand of image management tools as efficient as those text search engines. Decades of research on image retrieval has found there is a significant gap between the existing content based image retrieval and semantic interpretation of images by human. As a result, recent research on image retrieval has shifted to semantic image retrieval. Many semantic image retrieval models have been proposed, however, these methods are still alienated from the widely accepted text based retrieval method. In this paper, we propose to unite the semantic image retrieval model with text based retrieval using a novel region based inverted file indexing method. For this purpose, images are translated into textual documents which are then indexed and retrieved the same way as the conventional text based search. Results show that our method not only provides text based search efficiency, but also better performance than the conventional low level image retrieval.