Toward an estimation of user tagging credibility for social image retrieval
MM 2014 - Proceedings of the 2014 ACM Conference on Multimedia
1021 - 1024
Item Usage Stats
Existing image retrieval systems exploit textual or/and visual information to return results. Retrieval is mostly focused on data themselves and disregards the data sources. In Web 2.0 platforms, the quality of annotations provided by different users can vary strongly. To account for this variability, we complement existing methods by introducing user tagging credibility in the retrieval process. Tagging credibility is automatically estimated by leveraging a large set of visual concept classifiers learned with Overfeat, a convolutional neural network (CNN) feature. A good image retrieval system should return results that are both relevant and diversified and here we tackle both challenges. Classically, we diversify results by using a k-Means algorithm and increase relevance by favoring images uploaded by users with good credibility estimates. Evaluation is performed on DIV400, a publicly available social image retrieval dataset and shows that our method is competitive with existing approaches.
Convolutional neural network
Image retrieval systems
Social image retrievals
Published Version (Please cite this version)https://doi.org/10.1145/2647868.2655033
Showing items related by title, author, creator and subject.
Çavuş, Özge; Aksoy, Selim (IEEE, 2008-04)Son yıllarda çok geniş veri tabanlarının kullanımıyla birlikte içerik tabanlı görüntü indekslemesi ve erişimi önemli bir araştırma konusu halini almıştır. Bu çalışmada, görüntü indekslemesi için sahne sınıflandırmasını baz ...
Altıngövde, İsmail Şengör; Özcan, Rıfat; Ulusoy, Özgür (Springer, 2009-04)We compare the term- and document-centric static index pruning approaches as described in the literature and investigate their sensitivity to the scoring functions employed during the pruning and actual retrieval stages. ...
Sener F.; Ikizler-Cinbis, N. (Elsevier Ltd, 2014)Text-based image retrieval may perform poorly due to the irrelevant and/or incomplete text surrounding the images in the web pages. In such situations, visual content of the images can be leveraged to improve the image ...