Efficient Content Based Image Retrieval System with Metadata Processing

The content based image retrieval (CBIR) is one of the most popular, rising research areas of the digital image processing. Most of the available image search tools, such as Google Images and Yahoo! Image search, are based on textual annotation of images. In these tools, images are manually annotated with keywords and then retrieved using text-based search methods. The performances of these systems are not satisfactory. The goal of CBIR is to extract visual content of an image automatically, like color, texture, or shape. This paper aims to introduce the problems and challenges concerned with the design and the creation of CBIR systems, which is based on the accurate image search mechanism. For efficient data management, a system is proposed which generates metadata for image contents. This system is using Content-Based Image Retrieval System (CBIR) based on Mpeg-7 descriptors. First, low-level features are extracted from the query image without metadata and the images with similar low-level features are retrieved from the CBIR system. Metadata of the result images which are similar to the query image are extracted from the metadata database. From the resulting metadata, common keywords are extracted and proposed as the keywords for the query image. The extraction of color features from digital images depends on an understanding of the theory of color and the representation of color in digital images. Color spaces are an important component for relating color to its representation in digital form. The transformations between different color spaces and the quantization of color information are primary determinants of a given feature extraction method. The approach is found to be robust in terms of accuracy and is 92.4% amongst five categories.