Content-based image retrieval (CBIR) is the application of computer vision techniques to the image retrieval problem, that is, the problem of searching for digital images in large databases. “Content-based” means that the search analyzes the contents of the image rather than the metadata such as keywords, tags, or descriptions associated with the image. The term “content” in this context might refer to colors, shapes, textures, or any other information that can be derived from the image itself. CBIR is desirable because searches that rely purely on metadata are dependent on annotation quality and completeness. Having humans manually annotate images by entering keywords or metadata in a large database can be time consuming and not capture the keywords desired to describe the image. The evaluation of the effectiveness of keyword image search is subjective and has not been well-defined. In the same regard, CBIR systems have similar challenges in defining success. In this project, retrieval images are carried out by certain feature extraction methods such as Color Feature extraction, Texture feature Extraction and Shape feature Extraction has been performed. The evaluation of the effectiveness of keyword image search is subjective and has not been well-defined. Initially the text-based information is fused with images by Non-Subsampled Contourlet Transform. In Color Feature extraction, Color Histrogram, Color Moments and Color Auto Correlogram have been proposed. For Texture feature Extraction Gabor wavelet and Haar Wavelet process have been implemented. Finally for Shape feature Extraction, Fourier Descriptor, Circularity features have been proposed. The extracted features are then optimized by Co occurance matrix, where features are optimized and approximated to relevant features. This features are finally classified with similarity computation Euclidian distance. So as to can retrieve the relevant images from the databases.