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The Fast Development of the image capturing in digital form leads to the availability of large databases of images. The manipulation and management of images within these databases depend mainly on the user interface and the search algorithm used to search these huge databases for images, there are two search methods for searching within image databases: Text-Based and Content-Based. In this paper, we present a method for content-based image retrieval based on most used colors to extract image features. A preprocessing is applied to enhance the extracted features, which are smoothing, quantization and edge detection. Color quantization is applied using RGB (Red, Green, and Blue) Color Space to reduce the range of colors in the image and then extract the most used color from the image. In this approach, Color distance is applied using HSV (Hue, Saturation, Value) color space for comparing a query image with database images because it is the closest color space to the human perspective of colors. This approach provides accurate, efficient, less complex retrieval system.

In contour-based corner detectors, absolute curvature values of many corners either remain unaltered or change slightly under affine transformations. Moreover, affine-length of a curve is relatively invariant to affine transformations. This paper presents a novel corner matching technique using corners detected by contour-based detectors. For each corner we use its position, absolute curvature value, and affine-lengths between this corner and other corners on the same curve. The iterative matching procedure tries to find three corner matches with minimum absolute curvature difference to calculate affine transformation parameters. Original corners are transformed with the estimated parameters prior to matching with the test corner set.

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