Wavelet-Transform Based K-Means Algorithm

Image classification is the process of grouping image pixels into categories or classes to produce a thematic representation. The objective of image classification is to identify the features occurring in an image in terms of the object or type of land cover. Different classification algorithms are available to classify image. Image classification algorithms divide an image into regions which have same properties. These algorithms are applied in many areas such as medical imaging, object identification in satellite images, traffic control systems, brake light detection, machine vision, face recognition, fingerprint recognition, etc. The performance of classification algorithms can be expressed in terms of their accuracy which is measured by confusion matrix. This paper describe the modified wavelet transform based k-means algorithm which gives batter result compare to unsupervised k-means algorithm because wavelet transform extract more features from the image.