dc.contributor.author |
Poulose Jacob,K |
|
dc.contributor.author |
Vimina, E R |
|
dc.date.accessioned |
2014-06-11T09:01:55Z |
|
dc.date.available |
2014-06-11T09:01:55Z |
|
dc.date.issued |
2013-03 |
|
dc.identifier.uri |
http://dyuthi.cusat.ac.in/purl/3884 |
|
dc.description |
Journal of Image and Graphics, Volume 1, No.1, March, 2013 |
en_US |
dc.description.abstract |
This paper proposes a region based image
retrieval system using the local colour and texture features
of image sub regions. The regions of interest (ROI) are
roughly identified by segmenting the image into fixed
partitions, finding the edge map and applying
morphological dilation. The colour and texture features of
the ROIs are computed from the histograms of the
quantized HSV colour space and Gray Level co- occurrence
matrix (GLCM) respectively. Each ROI of the query image
is compared with same number of ROIs of the target image
that are arranged in the descending order of white pixel
density in the regions, using Euclidean distance measure for
similarity computation. Preliminary experimental results
show that the proposed method provides better retrieving
result than retrieval using some of the existing methods. |
en_US |
dc.description.sponsorship |
Cochin University of Science and Technology |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
Journal of Image and Graphics |
en_US |
dc.subject |
Content based image retrieval (CBIR) |
en_US |
dc.subject |
HSV color space |
en_US |
dc.subject |
Regions of Interest |
en_US |
dc.subject |
Colour histogram |
en_US |
dc.subject |
Euclidean distance |
en_US |
dc.subject |
GLCM. |
en_US |
dc.title |
Content Based Image Retrieval Using Low Level Features of Automatically Extracted Regions of Interest |
en_US |
dc.type |
Article |
en_US |