22-04-2010, 12:18 AM
Content-based image retrieval
Content-based image retrieval (CBIR), also known as query by image content (QBIC) and content-based visual information retrieval (CBVIR) is the application of computer vision to the image retrieval problem, that is, the problem of searching for digital images in large databases.
"Content-based" means that the search will analyze the actual contents of the image. The term 'content' in this context might refer colors, shapes, textures, or any other information that can be derived from the image itself. Without the ability to examine image content, searches must rely on metadata such as captions or keywords, which may be laborious or expensive to produce.
CBIR software systems and techniques
Query techniques
Different implementations of CBIR make use of different types of user queries.
Query by example
Query by example is a query technique that involves providing the CBIR system with an example image that it will then base its search upon. The underlying search algorithms may vary depending on the application, but result images should all share common elements with the provided example.
Options for providing example images to the system include:
A preexisting image may be supplied by the user or chosen from a random set.
The user draws a rough approximation of the image they are looking for, for example with blobs of color or general shapes.
This query technique removes the difficulties that can arise when trying to describe images with words.
Other query methods
Other methods include specifying the proportions of colors desired (e.g. "80% red, 20% blue") and searching for images that contain an object given in a query image.
CBIR systems can also make use of relevance feedback, where the user progressively refines the search results by marking images in the results as "relevant", "not relevant", or "neutral" to the search query, then repeating the search with the new information.
Content comparison techniques
The sections below describe common methods for extracting content from images so that they can be easily compared. The methods outlined are not specific to any particular application domain.
Color
Examining images based on the colors they contain is one of the most widely used techniques because it does not depend on image size or orientation. Color searches will usually involve comparing color histograms, though this is not the only technique in practice.
Texture
Texture measures look for visual patterns in images and how they are spatially defined. Textures are represented by texels which are then placed into a number of sets, depending on how many textures are detected in the image. These sets not only define the texture, but also where in the image the texture is located.
Shape
Shape does not refer to the shape of an image but to the shape of a particular region that is being sought out. Shapes will often be determined first applying segmentation or edge detection to an image. In some cases accurate shape detection will require human intervention because methods like segmentation are very difficult to completely automate.
The CBIR system is developed using ASP.NET with C#. It can be developed in other programming languages like J2EE.
