Name
Abstract | ||
|---|---|---|
Defining a lexicon of high-level concepts is the first step for data collection and model construction in concept-based image retrieval. Differences of semantic gaps among concepts are well worth considering. By measuring consistency in visual space and textual space, concepts with small semantic gap can be obtained. Considering so many diverse concepts in large-scale image dataset, we construct a lexica family of high-level concepts with small semantic gap based on different low-level features and different consistency measurements. In this lexica family, the lexica are independent to each other and mutually complementary. It provides helpful suggestions about data collection, feature selection and search model construction for large-scale image retrieval. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICME.2009.5202784 | ICME |
Keywords | Field | DocType |
concept-based image retrieval,data collection,different consistency measurement,large-scale image retrieval,small semantic gap,different low-level feature,lexica family,large-scale image dataset,high-level concept,semantic gap,feature extraction,data analysis,text analysis,construction industry,feature selection,indexing terms,artificial neural networks,visualization,image retrieval,data mining,visual space | Visual space,Data collection,Feature selection,Information retrieval,Visualization,Computer science,Semantic gap,Image retrieval,Feature extraction,Lexicon | Conference |
ISSN | Citations | PageRank |
1945-7871 | 0 | 0.34 |
References | Authors | |
4 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiemin Liu | 1 | 0 | 0.34 |
| Qi Tian | 2 | 237 | 10.44 |
| Yijuan Lu | 3 | 732 | 46.24 |
| Changhu Wang | 4 | 1296 | 70.36 |
| Lei Zhang | 5 | 1754 | 89.83 |
| Xiaokang Yang | 6 | 3581 | 238.09 |
| Shipeng Li | 7 | 3902 | 252.94 |