MIRACLE-FI at ImageCLEFphoto 2008: Experiences in merging Text-based and Content-based Retrievals

For this year’s Image CLEF Photo Retrieval task, we have prepared 5 submission runs to help us assess the effectiveness of 1) image content-based retrieval, and 2) text-

Run 7 – Mixed: A visual and a semantic retrieval are applied as in the runs 5 and 6 and the results are combined using percentages(80-20) for the text retrieval and image

To generate the feature vectors at different levels of abstraction, we extract both visual concept- based feature based on a “bag of concepts” model comprising color and texture

• Content Based Image Retrieval (CBIR) systems use low-level image features to return images similar to an image used as example.. The main problem of this approach is that

We first experimented with a text-based image retrieval approach with query extension, where the expansion terms are automatically selected from a knowledge base that

NAR requires that every similar image (s i > 0 ) in the searched data set or image domain is ranked by the tested CBIRS, or in other words the result list must contain all

The metric space model of similarity has already proved to be a very powerful concept for expressing many different forms of similarity of vectors, strings, sets and other data

As the purpose of a CBIR-system is to facilitate retrieval of images similar to some user’s information need, based on his/her visual experience of the image, comparing images