Name
Abstract | ||
|---|---|---|
The amount and variety of multimedia data such as images, movies and music available on over social networks are increasing rapidly. However, the ability to analyze and exploit these unorganized multimedia data remains inadequate, even with state-of-the-art media processing techniques. Our finding in this paper is that the emerging social curation service is a promising information source for the automatic understanding and mining of images distributed and exchanged via social media. One remarkable virtue of social curation service datasets is that they are weakly supervised: the content in the service is manually collected, selected and maintained by users. This is very different from other social information sources, and we can utilize this characteristics for media content mining without expensive media processing techniques. In this paper we present a machine learning system for predicting view counts of images in social curation data as the first step to automatic image content evaluation. Our experiments confirm that the simple features extracted from a social curation corpus are much superior in terms of count prediction than the gold-standard image features of computer vision research. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICDM.2012.37 | ICDM |
Keywords | Field | DocType |
media content mining,social network,social curation service datasets,towards automatic image understanding,state-of-the-art media,social information source,expensive media,social media,social curation corpus,social curation service,social curation data,social curation,learning artificial intelligence,feature extraction,data mining,image processing | Data science,Data mining,Social network,Computer science,Image content,Image processing,Artificial intelligence,Social media,Feature (computer vision),Feature extraction,Exploit,Simple Features,Machine learning | Conference |
ISSN | Citations | PageRank |
1550-4786 | 12 | 0.87 |
References | Authors | |
14 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Katsuhiko Ishiguro | 1 | 186 | 16.76 |
| Akisato Kimura | 2 | 244 | 28.03 |
| Koh Takeuchi | 3 | 59 | 11.29 |