Image Retrieval System by Using CWT and Support Vector Machines

This paper presents an image retrieval system based on dual tree complex wavelet transform (CWT) and support vector machines (SVM). There are two attributes of image retrieval system. First, images that a user needs through query image are similar to a group of images with the same conception. Second, there exists non-linear relationship between feature vectors of different images. Standard DWT (Discrete Wavelet Transform), being non-redundant, is a very powerful tool for many non-stationary Signal Processing applications, but it suffers from three major limitations; 1) shift sensitivity, 2) poor directionality, and 3) absence of phase information. To reduce these limitations, Complex Wavelet Transform (CWT). The initial motivation behind the development of CWT was to avail explicitly both magnitude and phase information. At the first level, for low level feature extraction, the dual tree complex wavelet transform will be used for both texture and color-based features. At the second level, to extract semantic concepts, we will group medical images with the use of one against all support vector machines. We are used here Euclidean distance for to measure the similarity between database features and query features. Also we can use a correlation-based distance metric for comparison of SVM distances vectors.The proposed approach has superior retrieval performance over the existing linear feature combining techniques.