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Identification of Similar Looking Bulk Split Grams using GLCM and CGLCM Texture Features

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Pushpalatha K. R., Asha Gowda Karegowda, D. Ramesh

Pushpalatha K R., Asha Gowda Karegowda and D Ramesh. Identification of Similar Looking Bulk Split Grams using GLCM and CGLCM Texture Features. International Journal of Computer Applications 167(6):30-36, June 2017. BibTeX

	author = {Pushpalatha K. R. and Asha Gowda Karegowda and D. Ramesh},
	title = {Identification of Similar Looking Bulk Split Grams using GLCM and CGLCM Texture Features},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2017},
	volume = {167},
	number = {6},
	month = {Jun},
	year = {2017},
	issn = {0975-8887},
	pages = {30-36},
	numpages = {7},
	url = {},
	doi = {10.5120/ijca2017914328},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Content based image retrieval (CBIR) is an automated way to retrieve images based on the visual content or image features itself. Visual inspection of food type is tiresome and time consuming task. This paper presents the retrieval of similar looking bulk split gram images using Grey Level Co-occurrence Matrix (GLCM) and Color Grey Level Co-occurrence Matrix (CGLCM) texture features. Texture feature matching procedure is based on three distance measures namely, Euclidean distance, Canberra distance and City block distance. The performance of a retrieved image is measured in terms of Precision. Experimental results show that the CGLCM provides better retrieving result than GLCM.


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CBIR, GLCM, CGLCM, Euclidean Distance, Canberra Distance, City Block Distance, Precision.