Name
Abstract | ||
|---|---|---|
One major challenge in the content-based image retrieval (CBIR) and computer vision research is to bridge the so-called "semantic gap" between low-level visual features and high-level semantic concepts, that is, extracting semantic concepts from a large database of images effectively. In this paper, we tackle the problem by mining the decisive feature patterns (DFPs). Intuitively, a decisive feature pattern is a combination of low-level feature values that are unique and significant for describing a semantic concept. Interesting algorithms are developed to mine the decisive feature patterns and construct a rule base to automatically recognize semantic concepts in images. A systematic performance study on large image databases containing many semantic concepts shows that our method is more effective than some previously proposed methods. Importantly, our method can be generally applied to any domain of semantic concepts and low-level features. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/s00530-006-0029-x | Multimedia Systems |
Keywords | Field | DocType |
multimedia semantic understanding · multimedia data mining · multimedia information retrieval · multimedia processing and pattern recognition,rule based,computer vision,semantic gap,pattern recognition | Semantic similarity,Semantic technology,Information retrieval,Computer science,Semantic gap,Image retrieval,Multimedia information retrieval,Semantic grid,Semantic computing,Multimedia data mining | Journal |
Volume | Issue | ISSN |
11 | 4 | 1432-1882 |
Citations | PageRank | References |
7 | 0.50 | 12 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Wang | 1 | 51 | 4.27 |
| Aidong Zhang | 2 | 2970 | 405.63 |