Median Gray Level Value for Texture Classification
Shrikant Bhosle2024, IJIT Journal
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Abstract
A new texture feature extraction method proposed in this paper called Median Gray Level Value method. It is an effective texture classification method due to its high discrimination capability and low computational complexity. The MGLV method divides the image into 3˟3 region and compares the pixel intensity value with median value of region. It extracts various local texture features from image. These are median features (MF), Symmetric Intensity Difference (SID) features, features. All these features are robust to rotation invariant and illumination invariant texture classification. The MGLV method use K-Nearest Neighbors (KNN) and Naïve Bayes (NB) classifier for texture classification. Experiment result show that, the proposed MGLV method outperform for classification of normal texture image. It gives 92.00% result using Kylberg texture database. This indicate that MGLV method extract more detail texture information of image. Experiment result also shows more distinctive performance for rotation invariant and illumination invariant texture classification. It gives 89.74% result for rotation invariant texture classification using Brodatz texture database and 41.25% result for illumination invariant texture classification using Kth-Tips texture database.
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