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 techniques to the image retrieval problem, that is, the problem of searching for digital images in large databases. (see this survey for a recent scientific overview of the CBIR field). Content based image retrieval is opposed to concept based approaches (see concept based image indexing).
"Content-based" means that the search will analyze the actual contents of the image rather than the metadata such as keywords, tags, and/or descriptions associated with the image. The term 'content' in this context might refer to colors, shapes, textures, or any other information that can be derived from the image itself. CBIR is desirable because most web based image search engines rely purely on metadata and this produces a lot of garbage in the results. Also having humans manually enter keywords for images in a large database can be inefficient, expensive and may not capture every keyword that describes the image. Thus a system that can filter images based on their content would provide better indexing and return more accurate results.
Other articles related to "images":
... applications into the and law enforcement markets for the purpose of identifying and censoring images with skin-tones and shapes that could indicate the presence of nudity ...
Famous quotes containing the word image:
“The image cannot be dispossessed of a primordial freshness, which idea can never claim. An idea is derivative and tamed. The image is in the natural or wild state, and it has to be discovered there, not put there, obeying its own law and none of ours. We think we can lay hold of image and take it captive, but the docile captive is not the real image but only the idea, which is the image with its character beaten out of it.”
—John Crowe Ransom (18881974)