Name
Abstract | ||
|---|---|---|
The recommendation of trending images has become a popular feature used by commercial search engines to attract public attention. By browsing through trending images, search engine users can discover trending events at a glance. However, the selection of trending images is very challenging and remains an open issue. Most existing work is highly dependent on editorial efforts, though some preliminarily identify a few plain features for trending images. In this paper, we investigate a set of perceptual factors that can distinguish trending images from common ones. We propose a set of trending-aware features based on several common criteria, which reflect the characteristics of trending images. We further construct a manually labeled dataset based on a commercial search engine's query log over a two-week timespan. We evaluate our proposed method on this dataset and the results demonstrate its effectiveness. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ICME.2015.7177413 | 2015 IEEE International Conference on Multimedia and Expo (ICME) |
Keywords | Field | DocType |
Trending image selection,image search,trending-aware features | Data mining,World Wide Web,Search engine,Information retrieval,Computer science,Visualization,Support vector machine,Feature extraction,Common Criteria,Perception,Market research | Conference |
ISSN | Citations | PageRank |
1945-7871 | 1 | 0.35 |
References | Authors | |
17 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dongfei Yu | 1 | 1 | 0.35 |
| Xinmie Tian | 2 | 487 | 38.43 |
| Tao Mei | 3 | 4702 | 288.54 |
| Yong Rui | 4 | 7052 | 449.08 |