| dc.contributor.author |
Kannan, Balakrishnan |
|
| dc.contributor.author |
Unnikrishnan, A |
|
| dc.contributor.author |
Bino, Sebastian V |
|
| dc.date.accessioned |
2014-07-22T06:01:16Z |
|
| dc.date.available |
2014-07-22T06:01:16Z |
|
| dc.date.issued |
2012-04 |
|
| dc.identifier.uri |
http://dyuthi.cusat.ac.in/purl/4199 |
|
| dc.description |
International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012 |
en_US |
| dc.description.abstract |
Grey Level Co-occurrence Matrices (GLCM) are one of the earliest techniques used for image texture
analysis. In this paper we defined a new feature called trace extracted from the GLCM and its implications
in texture analysis are discussed in the context of Content Based Image Retrieval (CBIR). The theoretical
extension of GLCM to n-dimensional gray scale images are also discussed. The results indicate that trace
features outperform Haralick features when applied to CBIR. |
en_US |
| dc.description.sponsorship |
Cochin University of Science and Technology |
en_US |
| dc.language.iso |
en |
en_US |
| dc.subject |
Grey Level Co-occurrence Matrix |
en_US |
| dc.subject |
Texture Analysis |
en_US |
| dc.subject |
Haralick Features |
en_US |
| dc.subject |
N-Dimensional Co-occurrence Matrix |
en_US |
| dc.subject |
Trace |
en_US |
| dc.subject |
CBIR |
en_US |
| dc.title |
Grey Level Co-Occurrence Matrices: Generalisation And Some New Features |
en_US |
| dc.type |
Article |
en_US |