An image retrieval system is a computer system for browsing, searching, and retrieving images from a large database of digital images. Robust retrieval of natural, geographic and medical images using a supervised classifier that focuses on the extracted features is proposed. Gray Level Co-occurrence Matrix (GLCM), Scale Invariant Feature Technique (SIFT) and Moment Invariant Features are implemented to extract features from natural images and GLCM and Gabor feature extraction is performed on medical images. Then, these features are passed through the SVM classifier. SVM classifies whether the input is a geographic or natural or medical image. Based on the SVM result, the recovery process is carried out with Euclidean distance. Performance comparison is done with standard features such as color and texture. Keywords-Gabor, GLCM, invariant moment, SIFT, SVM.I. INTRODUCTIONContent-based image retrieval is a technique that uses visual contents to search images from large-scale image databases based on user interests. It has been an active and rapidly evolving area of ​​research since the 1990s. A necessity for developing a successful CBIR system is the extraction of discriminative features to describe the images in the database. As such, the development of feature extraction algorithms has dominated the literature in the field, where the ultimate goal is to retrieve visually similar images. In this paper, retrieval is performed for natural and geographic images using SIFT, GLCM and moment invariant techniques. similarly, GLCM and Gabor techniques are adopted for medical images. The advantages of using these feature extraction algorithms are better error tolerance with fewer matches, reliability, efficient and optimal image matching task.II. ...... middle of paper ...... of sight words', IEEE Geosci. Télésens. Lett, vol. 7, no. 2, pages 366 to 370.[10] Goncalves J and Goncalves H (2011), “Automatic Image Registration via Image Segmentation and SIFT”, IEEE Trans. Geosci.Remote Sens., vol. 49, no. 7, pp. 2589-2600.[11] Gool LJ, Moons T and Ungureanu D (2000), “Affine/photometric invariants for planar intensity models”, in Proc. EUR. Conf. Calculate. Vis., pp. 642-651.[12] Hongyu Y and Wen C (2004), “Remote sensing image retrieval based on Gabor texture feature classification, in Proc. Int. Conf. Signal Process., pp. 733-736.[13] Lindeberg T (1998), “Feature detection with automatic scale selection”, Int. J. Computer science. Vis., vol. 30, no. 2, pages 79 to 116.[14] H.Lang, R. Hanka and HHS Ip (2003), “Histological image retrieval based on semantic content analysis”, IEEE Trans. Inf. Technology. Biomed., vol. 7, no. 1, p.p... 26–36.