Name
Abstract | ||
|---|---|---|
In this paper, we propose a multiple-level image semantics classification method. The multiple-level image semantics classifier is constructed according to a hierarchical semantics tree. A semantics tree is defined according to the individual user’s habit of managing files. So it is personalized. The classification features are selected by calculating information entropy of images. The hierarchical classifier is constructed according to a class correlation measure. This measure considers both the relation of the classifiers between different hierarchical levels and the relation between the classifiers at the same level. The unlabelled pictures can be classified top-down and assigned to corresponding class and semantic labels. In our experiment binary SVM is used. The hierarchical classifier is built by selecting meta-classifiers with the combinations that have better performance. The result shows that the hierarchical classifier is more effective than a flat method. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11811305_20 | ADMA |
Keywords | Field | DocType |
user-centered image semantics classification,multiple-level image semantics classifier,hierarchical classifier,semantics tree,different hierarchical level,corresponding class,classification feature,class correlation measure,flat method,multiple-level image semantics classification,hierarchical semantics tree,information entropy,top down | Data mining,Computer science,Image processing,Artificial intelligence,Hierarchical classifier,Contextual image classification,Classifier (linguistics),Entropy (information theory),Binary number,Pattern recognition,Support vector machine,Semantics,Machine learning | Conference |
Volume | ISSN | ISBN |
4093 | 0302-9743 | 3-540-37025-0 |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongli Xu | 1 | 502 | 85.92 |
| De Xu | 2 | 158 | 13.08 |
| Fangshi Wang | 3 | 21 | 4.74 |