As more and more social network users interact through Internet Memes, an emerging popular type of captioned images, there is a growing need for users to quickly retrieve the right Meme for a given situation. As opposed conventional image search, visually similar Memes may reflect different concepts. Intent is sometimes captured by user annotations (e.g., tags), but these are often incomplete and ambiguous. Thus, a deeper analysis of the relations among Memes is required for an accurate, custom search. To address this problem, we present SimMeme, a Meme-dedicated search engine. SimMeme uses a generic graph-based data model that aligns various types of information about the Memes with a semantic ontology. A novel similarity measure that effectively considers all incorporated data is employed and serves as the foundation of our system. Our experimental results achieve using common evaluation metrics and crowd feedback, over a large repository of real-life annotated Memes, show that in the task of Meme retrieval, SimMeme outperforms state-of-the-art solutions for image retrieval.
|Title of host publication||Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019|
|Publisher||IEEE Computer Society|
|Number of pages||12|
|State||Published - Apr 2019|
|Event||35th IEEE International Conference on Data Engineering, ICDE 2019 - Macau, China|
Duration: 8 Apr 2019 → 11 Apr 2019
|Name||Proceedings - International Conference on Data Engineering|
|Conference||35th IEEE International Conference on Data Engineering, ICDE 2019|
|Period||8/04/19 → 11/04/19|
Bibliographical notePublisher Copyright:
© 2019 IEEE.
- Information network
- Internet memes