A Sub-block Based Image Retrieval Using Modified Integrated Region Matching.

Content Provider	CUSAT-Thesis
Author	Poulose Jacob, K. Vimina, E. R.
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
File Format	PDF
Language	English
Publisher	International Journal of Computer Science Issues (IJCSI)
Access Restriction	Open
Subject Keyword	CBIR Colour histogram Edge histogram descriptor Euclidean distance GLCM IRM similarity
Content Type	Text
Resource Type	Article