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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.

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 for a scientific ...

The objective of CBIR is to excerpt visual content of an image inevitably, like color, shape or texture. The CBIR tools can be utilized in numerous applications such as digital libraries, photo sharing sites and crime prevention.

Image retrieval techniques can be categorized as Text-Based Image Retrieval (TBIR) and Content-Based Image Retrieval (CBIR). TBIR is used to define both metadata of the image and image content such as image file name, size and format.

Content Based Image Retrieval In content based image retrieval, images are searched and retrieved on the basis of similarity of their visual contents to a query image using features of the image. A feature extraction module is used to extract low-level image features from the images in the collection.

Content-Based Image Retrieval (CBIR) is a way of retrieving images from a database. In CBIR, a user specifies a query image and gets the images in the database similar to the query image. To find the most similar images, CBIR compares the content of the input image to the database images.

One of the main advantages of the CBIR approach is the possibility of an automatic retrieval process, instead of the additional keyword-based approach, which usually requires very laborious and time-consuming previous annotation of database images.