Name
Abstract | ||
|---|---|---|
Images in reading materials make the content come alive. Aside from providing additional information to the text, reading mate- rial containing illustration engages our spatial memory, increases memory retention of the material. However, despite the plethora of available multimedia, adding illustrations to text continues to be a difficult task for the amateur content publisher. To address this problem, we present a semantic-aware image discovery and in- sertion system for custom publishing. Compared to image search engines, our system has the advantage of being able to discern a- mong different topics within a long text passage and recommend the most relevant images for each detected topic with semantic vi- sual words based relevance. |
Year | Venue | Field |
|---|---|---|
2015 | RecSys Posters | World Wide Web,Information retrieval,Computer science,Memory retention,Amateur,Artificial intelligence,Publishing,Machine learning,Aside |
DocType | Citations | PageRank |
Conference | 4 | 0.49 |
References | Authors | |
5 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lei Liu | 1 | 50 | 8.33 |
| Jerry Liu | 2 | 8 | 1.25 |
| Shanchan Wu | 3 | 44 | 6.46 |