• Rutuja V Naik
• Payel Saha.

Digital image has numerous features. A feature is an image representative that can capture certain visual property of the image. Feature extraction basically means collection of certain properties from large amount of properties, Only those properties/features are extracted that are essential for image accurately. Extraction of Features is done on the base of their texture, color, shape. Texture is an important and superior feature for image feature extraction as compared to color, as color features are not sufficient to identify the image as dissimilar images may still have similar histograms. Texture is a recurring form of data or structure with consistent breaks. Collecting texture feature from image is called texture feature extraction. There are different algorithm like Structural, spectral and Statistical for extraction of Texture features. The most popular way of extracting Texture features of selected image by statistical method is done by second order statics based Gray Level Co-occurrence Matrix (GLCM). In this paper Energy (Angular Second Moment), Contrast, Entropy and Variance are computed using GLCM Algorithm. As per the result, High accuracy rate is achieved when the extraction of texture features is done using GLCM. Therefore less computation time is required and hence this method can be proficiently used for real time pattern detection and recognition applications.

GLCM Algorithm, Texture features, Image feature extraction.

"TEXTURE FEATURE EXTRACTION OF IMAGE USING GRAY LEVEL CO-OCCURRENCE MATRIX (GLCM)", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.282-287, June 2019, Available :http://www.jetir.org/papers/JETIR1906I71.pdf

"TEXTURE FEATURE EXTRACTION OF IMAGE USING GRAY LEVEL CO-OCCURRENCE MATRIX (GLCM)", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp282-287, June 2019, Available at : http://www.jetir.org/papers/JETIR1906I71.pdf