摘要：This study attempts to provide fast indexing technique which will help to retrieve images from the database quickly and focuses on how to retrieve most relevant images from the database. The need for efficient Content-based Image Retrieval (CBIR) has increased tremendously in many application areas such as biomedicine, military, commerce, education and web image classification and searching. The semantic gap is the greatest challenge in the CBIR. The semantic gap is the lack of coincidence between the information that one can extract from the visual data and the interpretation that the same data have for a user in a given situation. The CBIR uses the visual contents of an image such as color, shape, texture and spatial layout to represent and index the image. In typical content-based image retrieval systems, the visual contents of the images in the database are extracted and described by multi-dimensional feature vectors. The feature vectors of the images in the database form a feature database. To retrieve images, users provide the retrieval system with example images or sketched figures. The system then changes these examples into its internal representation of feature vectors. The similarities/distances between the feature vectors of the query example or sketch and those of the images in the database are then calculated and retrieval is performed with the aid of an indexing scheme. The indexing scheme provides an efficient way to search for the image database.
