![ARTICLE
• Content-based image retrieval, 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[1] for a scientific overview of the CBIR field). Content-based image retrieval is opposed to
traditional concept-based approaches (see Concept-based image indexing). "Content-based" means
that the search analyzes the contents of the image rather than the metadata such as keywords,
tags, 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 searches that rely purely on metadata are dependent on annotation quality and
completeness. Having humans manually annotate images by entering keywords or metadata in a
large database can be time consuming and may not capture the keywords desired to describe the
image. The evaluation of the effectiveness of keyword image search is subjective and has not been
well-defined. In the same regard, CBIR systems have similar challenges in defining success.[2]
"Keywords also limit the scope of queries to the set of predetermined criteria." and, "having been
set up" are less reliable than using the content itself.[3] The most common method for comparing
two images in content-based image retrieval (typically an example image and an image from the
database) is using an image distance measure. An image distance measure compares the similarity
of two images in various dimensions such as color, texture, shape, and others. For example, a
distance of 0 signifies an exact match with the query, with respect to the dimensions that were
considered. As one may intuitively gather, a value greater than 0 indicates various degrees of
similarities between the images. Search results then can be sorted based on their distance to the
queried image.[14] Many measures of image distance (Similarity Models) have been
developed.[19]](https://image.slidesharecdn.com/contentbasedimageretrieval-210726085037/85/Content-based-image-retrieval-2-320.jpg)
Content based image retrieval
- 1. content based image retrieval
- 2. ARTICLE • Content-based image retrieval, 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[1] for a scientific overview of the CBIR field). Content-based image retrieval is opposed to traditional concept-based approaches (see Concept-based image indexing). "Content-based" means that the search analyzes the contents of the image rather than the metadata such as keywords, tags, 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 searches that rely purely on metadata are dependent on annotation quality and completeness. Having humans manually annotate images by entering keywords or metadata in a large database can be time consuming and may not capture the keywords desired to describe the image. The evaluation of the effectiveness of keyword image search is subjective and has not been well-defined. In the same regard, CBIR systems have similar challenges in defining success.[2] "Keywords also limit the scope of queries to the set of predetermined criteria." and, "having been set up" are less reliable than using the content itself.[3] The most common method for comparing two images in content-based image retrieval (typically an example image and an image from the database) is using an image distance measure. An image distance measure compares the similarity of two images in various dimensions such as color, texture, shape, and others. For example, a distance of 0 signifies an exact match with the query, with respect to the dimensions that were considered. As one may intuitively gather, a value greater than 0 indicates various degrees of similarities between the images. Search results then can be sorted based on their distance to the queried image.[14] Many measures of image distance (Similarity Models) have been developed.[19]