Name
Abstract | ||
|---|---|---|
Image retrieval plays an increasingly important role in our daily lives. There are many factors which affect the quality of image search results, including chosen search algorithms, ranking functions, and indexing features. Applying different settings for these factors generates search result lists with varying levels of quality. However, no setting can always perform optimally for all queries. Th... |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TBDATA.2015.2497710 | IEEE Transactions on Big Data |
Keywords | Field | DocType |
Visualization,Search engines,Google,Big data,Search problems,Training,Algorithm design and analysis | Data mining,Phrase search,Computer science,Search engine indexing,Ranking (information retrieval),Preference learning,Artificial intelligence,Information retrieval,Semantic search,Beam search,Search analytics,Concept search,Machine learning | Journal |
Volume | Issue | Citations |
1 | 3 | 1 |
PageRank | References | Authors |
0.35 | 44 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xinmie Tian | 1 | 487 | 38.43 |
| Yijuan Lu | 2 | 732 | 46.24 |
| Nate Stender | 3 | 1 | 0.35 |
| Linjun Yang | 4 | 1556 | 65.20 |
| Dacheng Tao | 5 | 19032 | 747.78 |