Massive amounts of images textually annotated by different users are provided by social image websites, e.g., Flickr. Social images are always associated with various information, such as visual features, tags, and users. In this paper, we utilize hypergraph instead of ordinary graph to model social images, since relations among various information are more sophisticated than pairwise. Based on the hypergraph, we propose HIRT, a scalable image retrieval and tagging system, which uses Personalized PageRank to measure vertex similarity, and employs top-k search to support image retrieval and tagging. To achieve good scalability and efficiency, we develop parallel and approximate top-k search algorithms with quality guarantees. Experiments on a large Flickr dataset confirm the effectiveness and efficiency of our proposed system HIRT compared with existing state-of-the-art hypergraph based image retrieval system. In addition, our parallel and approximate top-k search methods are verified to be more efficient than the state-of-the-art methods and meanwhile achieve higher result quality.
|Titel||Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018|
|Publikationsdato||24 okt. 2018|
|Status||Udgivet - 24 okt. 2018|
|Begivenhed||34th IEEE International Conference on Data Engineering, ICDE 2018 - Paris, Frankrig|
Varighed: 16 apr. 2018 → 19 apr. 2018
|Konference||34th IEEE International Conference on Data Engineering, ICDE 2018|
|Periode||16/04/2018 → 19/04/2018|