Content-based image retrieval

The term "content" in this context might refer to colors, shapes, textures, or any other information that can be derived from the image itself.

The techniques, tools, and algorithms that are used originate from fields such as statistics, pattern recognition, signal processing, and computer vision.

[1] The earliest commercial CBIR system was developed by IBM and was called QBIC (Query By Image Content).

[7] While the storing of multiple images as part of a single entity preceded the term BLOB (Binary Large OBject),[8] the ability to fully search by content, rather than by description, had to await IBM's QBIC.

The interest in CBIR has grown because of the limitations inherent in metadata-based systems, as well as the large range of possible uses for efficient image retrieval.

[2] Initial CBIR systems were developed to search databases based on image color, texture, and shape properties.

Therefore, efforts in the CBIR field started to include human-centered design that tried to meet the needs of the user performing the search.

[1] Many CBIR systems have been developed, but as of 2006[update], the problem of retrieving images on the basis of their pixel content remains largely unsolved.

The underlying search algorithms may vary depending on the application, but result images should all share common elements with the provided example.

This type of open-ended task is very difficult for computers to perform - Lincoln may not always be facing the camera or in the same pose.

However, in general, image retrieval requires human feedback in order to identify higher-level concepts.

[6] Combining CBIR search techniques available with the wide range of potential users and their intent can be a difficult task.

The relative brightness of pairs of pixels is computed such that degree of contrast, regularity, coarseness and directionality may be estimated.

[22] An image is retrieved in CBIR system by adopting several techniques simultaneously such as Integrating Pixel Cluster Indexing, histogram intersection and discrete wavelet transform methods.