A multimedia database system stores and manages a large collection of multimedia data, such as audio, video, image, graphics, speech, text, document, and hypertext data, which contain text, text markups, and linkages. Multimedia database systems are increasingly common owing to the popular use of audio-video equipment, digital cameras, CD-ROMs, and the Internet. There are multimedia database systems include NASA’s EOS (Earth Observation System), various kinds of image and audio video databases, and Internet databases.
There is two main groups of multimedia indexing and retrieval systems which are as follows −
Description-based retrieval systems − It is used to build indices and perform object retrieval based on image descriptions, such as keywords, captions, size, and time of creation. Description-based retrieval is labor-intensive if performed manually. If automated, the results are typical of poor quality.
For instance, the assignment of keywords to images can be a difficult and arbitrary service. The latest development of Web-based image clustering and classification techniques has enhanced the quality of definition-based Web image retrieval because image surrounded text information and Web linkage information can be used to extract proper description and group images describing a similar theme together.
Content-based retrieval systems − It can support retrieval based on the image content, such as color histogram, texture, pattern, image topology, and the shape of objects and their layouts and locations within the image. Content-based retrieval facilitates visual characteristics to index images and improves object retrieval based on feature similarity, which is highly desirable in several applications.
In a content-based image retrieval system, there are often two kinds of queries − image sample-based queries and image feature specification queries. Image-sample-based queries find all of the images that are similar to the given image sample. This search analyzes the feature vector (or signature) extracted from the sample with the feature vectors of images that have been extracted and ordered in the image database.
Image feature specification queries define or draw picture features such as color, texture, or shape, which are translated into a feature vector to be connected with the feature vectors of the images in the database.
Content-based retrieval has wide applications, including medical diagnosis, weather prediction, TV production, Web search engines for images, and e-commerce. Some systems including QBIC (Query By Image Content), provide both sample-based and image feature requirements queries. There are also systems that support both content-based and description-based retrieval.