TEXTURE IMAGE CLASSIFICATION BY STATISTICAL FEATURES OF WAVELET

• Main Authors: Emmanuel Awuni Kolog, Samuel Nii Odoi Devine
• Format: Article
• Language: English
• Published: XLESCIENCE 2019-06-01
• Series: International Journal of Advances in Signal and Image Sciences
• Subjects: texture classification, discrete wavelet transform, k-nearest neighbor, classification, brodatz database
• Online Access: https://xlescience.org/index.php/IJASIS/article/view/36

An unidentified image sample is assigned to a recognized texture class is known as Texture Classification (TC). The main challenging task in TC is the non uniformity changes in orientation, visual appearance and scale. Texture is an important feature in computer analysis for the purpose of classification. In this paper, an efficient TC system based on Discrete Wavelet Transform (DWT) is presented. The performance of the system is evaluated by Brodatz database. At first, the DWT is used to decompose the input texture image for feature extraction at a particular decomposition level. From each sub-band coefficients statistical features are extracted. Finally, k-Nearest Neighbour (kNN) classifier is used for classification. Results show that a better classification accuracy of 94.72% is achieved by the features of 3rd level DWT and kNN classifier.