Now showing items 1-3 of 3
| Abstract: | This paper proposes a content based image retrieval (CBIR) system using the local colour and texture features of selected image sub-blocks and global colour and shape features of the image. The image sub-blocks are roughly identified by segmenting the image into partitions of different configuration, finding the edge density in each partition using edge thresholding, morphological dilation and finding the corner density in each partition. The colour and texture features of the identified regions are computed from the histograms of the quantized HSV colour space and Gray Level Co- occurrence Matrix (GLCM) respectively. A combined colour and texture feature vector is computed for each region. The shape features are computed from the Edge Histogram Descriptor (EHD). Euclidean distance measure is used for computing the distance between the features of the query and target image. Experimental results show that the proposed method provides better retrieving result than retrieval using some of the existing methods |
| Description: | International Journal of Advanced Science and Technology Vol. 48, November, 2012 |
| URI: | http://dyuthi.cusat.ac.in/purl/3877 |
| Files | Size |
|---|---|
| CBIR Using Loca ... es of Image Sub-blocks.pdf | (786.3Kb) |
| 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. |
| Description: | Journal of Image and Graphics, Volume 1, No.1, March, 2013 |
| URI: | http://dyuthi.cusat.ac.in/purl/3884 |
| Files | Size |
|---|---|
| Content Based I ... ed Regions of Interest.pdf | (1.651Mb) |
| Abstract: | This paper proposes a content based image retrieval (CBIR) system using the local colour and texture features of selected image sub-blocks and global colour and shape features of the image. The image sub-blocks are roughly identified by segmenting the image into partitions of different configuration, finding the edge density in each partition using edge thresholding, morphological dilation. The colour and texture features of the identified regions are computed from the histograms of the quantized HSV colour space and Gray Level Co- occurrence Matrix (GLCM) respectively. A combined colour and texture feature vector is computed for each region. The shape features are computed from the Edge Histogram Descriptor (EHD). A modified Integrated Region Matching (IRM) algorithm is used for finding the minimum distance between the sub-blocks of the query and target image. Experimental results show that the proposed method provides better retrieving result than retrieval using some of the existing methods |
| Description: | International Journal of Computer Science Issues (IJCSI) |
| URI: | http://dyuthi.cusat.ac.in/purl/3887 |
| Files | Size |
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| A Sub-block Bas ... grated Region Matching.pdf | (443.6Kb) |
Now showing items 1-3 of 3
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