Content based Image Retrieval Based on Curvelet and PCA using HTF

This paper presents a new approach for effective representation of search results of images from a pool of image database. It is difficult for search engines to interpret users’ intention only by query keywords and this leads to vague and irrelevant results which are far from acceptable. It is important to use visual information in order to solve the uncertainty in text-based image retrieval. Hence the content based image retrieval system comes as novel solution to the above problem. In this approach we propose to decompose a query image into several components and extract a set of its textural features. These features can be extracted in several ways. The most common method is to use a Gray Level Co-occurrence Matrix (GLCM). GLCM contains the second order statistical information of neighbouring pixels of an image. Initially, the curvelet approach is combined with principle component analysis retrieval system. The PCA employs for dimensionality reduction. The most similar matches are found from the database images as in the query by using Mahalonobis Distance measure.