Volume -I , Issue -X, April 2015


Author(s) :

K. Prasanthi Jasmine , P. Rajesh Kumar and K. Naga Prakash


This paper presents a novel image indexing and retrieval algorithm using Gaussian multi-resolution directional binary code (DBC) co-occurrence matrix and color histogram. DBC histogram captures only the patterns distribution in a texture while the spatial correlation between the pair of patterns is gathered by DBC Co-occurrence. Multi-resolution texture decomposition and co-occurrence calculation has been efficiently used in the proposed method where multi-resolution texture images are computed using Gaussian filter for collection of DBCs from these particular textures. Eventually, feature vectors are constructed by making into play the co-occurrence matrix that exists between binary patterns and color histogram which is constructed from the RGB spaces of the color image. The retrieval results of the proposed method have been tested by conducting two experiments on Corel-1K and MIT VisTex texture databases. The results after being investigated show a significant improvement in terms of their evaluation measures as compared to the existing features for image retrieal.


Multi-resolution features; Gaussian Filter; Directional Binary Code; Texture; Pattern Recognition; Feature Extraction; Local Binary Patterns; Image Retrieval.

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How to Cite this Paper? [APA Style]
K. Prasanthi Jasmine , P. Rajesh Kumar and K. Naga Prakash, (2015), COLOR AND MULTI-RESOLUTION DBC CO-OCCURRENCE MATRIX FOR IMAGE RETRIEVAL, Industrial Science Journal, http://industrialscience.org/Article.aspx?aid=65&vid=10, (April, 2015)
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