Browsing by Subject "Text-based systems"

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Open Access
Re-ranking of web image search results using a graph algorithm
(IEEE, 2008-12) Zitouni, Hilal; Sevil, Sare; Özkan, Derya; Duygulu, Pınar
We propose a method to improve the results of image search engines on the Internet to satisfy users who desire to see relevant images in the first few pages. The method re-ranks the results of text based systems by incorporating visual similarity of the resulting images. We observe that, together with many unrelated ones, results of text based systems include a subset of correct images, and this set is, in general, the largest one which has the most similar images compared to other possible subsets. Based on this observation, we present similarities of all images in a graph structure, and find the densest component that corresponds to the largest set of most similar subset of images. Then, to re-rank the results, we give higher priority to the images in the densest component, and rank the others based on their similarities to the images in the densest component. The experiments are carried out on 18 category of images from [8]. © 2008 IEEE.
Open Access
Tag suggestr: Automatic photo tag expansion using visual information for photo sharing websites
(Springer, 2008-12) Küçüktunç, Onur; Sevil, Sare G.; Tosun, A. Burak; Zitouni, Hilal; Duygulu, Pınar; Can, Fazlı
In this paper, we propose an automatic photo tag expansion system for the community photo collections, such as Flickr. Our aim is to suggest relevant tags for a target photograph uploaded to the system by a user, by incorporating the visual and textual cues from other related photographs. As the first step, the system requires the user to add only a few initial tags for each uploaded photo. These initial tags are used to retrieve related photos including the same tags in their tag lists. Then the set of candidate tags collected from a large pool of photos is weighted according to the similarity of the target photo to the retrieved photo including the tag. Finally, the tags in the highest rankings are used to automatically expand the tags of the target photo. The experimental results on Flickr photos show that, the use of visual similarity of semantically relevant photos to recommend tags improves the quality of suggested tags compared to only text-based systems. © 2008 Springer Berlin Heidelberg.