We present an unsupervised approach to automated story picturing. Semantic keywords are extracted from the story, an annotated image database is searched. Thereafter, a novel image ranking scheme automatically determines the importance of each image. Both lexical annotations and visual content play a role in determining the ranks. Annotations are processed using the Wordnet. A mutual reinforcement-based rank is calculated for each image. We have implemented the methods in our Story Picturing Engine (SPE) system. Experiments on large-scale image databases are reported. A user study has been performed and statistical analysis of the results has been presented.
|Original language||English (US)|
|Number of pages||22|
|Journal||ACM Transactions on Multimedia Computing, Communications and Applications|
|State||Published - Feb 2006|
All Science Journal Classification (ASJC) codes
- Hardware and Architecture
- Computer Networks and Communications