An improved content based image retreival

Content-based image retrieval (CBIR) systems demonstrate excellent performance at computing low-level features from pixel representations. Its output does not reflect the overall desire of the user. The systems perform poorly in extracting high-level (semantic) features that include objects and their meanings, actions and feelings. This is, referred to as the semantic gap, and has necessitated current research in CBIR systems towards retrieving images by Relevance Feedback. Content Based Image Retrieval is an interesting and most emerging field in the area of `Image Search´, finding similar images for the given query image from the image database. Color histogram is widely used for image indexing in Content based Image Retrieval. A color histogram describes the global color distribution of an image. It is very easy to compute and is insensitive to small changes in viewing positions. However, the histogram is not robust to large appearance changes. The proposed method uses color features of the image but to improve the retrieval results in terms of its accuracy relevance feedback is suggested.