Name
Abstract | ||
|---|---|---|
Multimedia data mining refers to pattern discovery, rule extraction and knowledge acquisition from multimedia database. Two typical tasks in multimedia data mining are of visual data classification and clustering in terms of semantics. Usually performance of such classification or clustering systems may not be favorable due to the use of low-level features for image representation, and also some improper similarity metrics for measuring the closeness between multimedia objects as well. This paper considers a problem of modeling similarity for semantic image clustering. A collection of semantic images and feed-forward neural networks are used to approximate a characteristic function of equivalence classes, which is termed as a learning pseudo metric (LPM). Empirical criteria on evaluating the goodness of the LPM are established. A LPM based k-Mean rule is then employed for the semantic image clustering practice, where two impurity indices, classification performance and robustness are used for performance evaluation. An artificial image database with 11 semantics is employed for our simulation studies. Results demonstrate the merits and usefulness of our proposed techniques for multimedia data mining. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/s00500-006-0086-2 | Soft Comput. |
Keywords | Field | DocType |
clustering system,multimedia object,multimedia data mining,multimedia database,visual data classification,semantic images · clustering and classification · learning pseudo metrics · neural networks,semantic image clustering,neural similarity metrics,semantic image,image representation,artificial image database,classification performance,neural network,feed forward neural network,characteristic function,k means | Data mining,Multimedia database,Computer science,Robustness (computer science),Artificial intelligence,Data classification,Equivalence class,Cluster analysis,Artificial neural network,Semantics,Knowledge acquisition,Machine learning | Journal |
Volume | Issue | ISSN |
11 | 4 | 1433-7479 |
Citations | PageRank | References |
8 | 0.53 | 13 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dianhui Wang | 1 | 1547 | 93.41 |
| Yong Soo Kim | 2 | 185 | 23.42 |
| Seok Cheon Park | 3 | 33 | 5.55 |
| Chul-Soo Lee | 4 | 40 | 5.30 |
| Yoon Kyung Han | 5 | 8 | 0.86 |