Poulose Jacob,K; Vimina, E R(International Journal of Advanced Science and Technology, November , 2012)
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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
Poulose Jacob,K; Vimina, E R(Journal of Image and Graphics, March , 2013)
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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
Poulose Jacob,K; Vimina, E R(International Journal of Computer Science Issues (IJCSI), January 1, 2013)
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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)