Name
Abstract | ||
|---|---|---|
Mutual information is one of the mostly used measures for evaluating image similarity. In this paper, we investigate the application of three different Tsallis-based generalizations of mutual information to analyze the similarity between scanned documents. These three generalizations derive from the Kullback-Leibler distance, the difference between entropy and conditional entropy, and the Jensen-Tsallis divergence, respectively. In addition, the ratio between these measures and the Tsallis joint entropy is analyzed. The performance of all these measures is studied for different entropic indexes in the context of document classification and registration. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.3390/e13091694 | ENTROPY |
Keywords | Field | DocType |
Tsallis entropy,mutual information,image similarity,document classification | Document classification,Template matching,Pattern recognition,Information diagram,Tsallis entropy,Mutual information,Artificial intelligence,Joint entropy,Total correlation,Pointwise mutual information,Mathematics | Journal |
Volume | Issue | ISSN |
13 | 9 | 1099-4300 |
Citations | PageRank | References |
8 | 0.62 | 22 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Màrius Vila | 1 | 12 | 1.36 |
| Anton Bardera | 2 | 141 | 12.22 |
| Miquel Feixas | 3 | 637 | 45.61 |
| Mateu Sbert | 4 | 1108 | 123.95 |