Image Retrieval Using Modified Haar Wavelet Transform and K Means Clustering
P. S. Malge · 2013
The Content Based Image Retrieval (CBIR) has been an active research area. Given a collection of images, it is to retrieve the images based on a query image, which is specified by content. In the proposed paper Wavelet Transform has been used, which is proved to be a very useful tool for image processing in recent years. It allows a function which may be described in terms of details that range from broad to narrow. In the proposed paper, we use modified Haar wavelet transformation for feature extraction of an image. The present method uses a new technique based on wavelet transformations by which a feature vector of size ten, characterizing texture feature of the images is constructed. Our method derives feature vector (10 signatures) for each image characterizing the texture feature of sub image from only three iterations of wavelet transforms. The K Means Clustering Algorithm is used to cluster the group of images based on feature vector of images by considering the minimum Euclidean distance. Our experiments are performed on texture images, and successful matching results are found.