Content Based Image Retrieval Techniques

CBIR techniques

In contrast to the text based approach of the systems, CBIR operates on a totally different principal, retrieving stored images from a collection by comparing features automatically extracted from the images themselves. The commonest features used are mathematical measures of color, texture or shape. A typical system allows users to formulate queries by submitting an example of the type of image being sought, though some offer alternatives such as selection from a palette or sketch input. The system then identifies those stored image whose feature values match those of the query most closely, and displace thumbnails of these images on the screen.


Color Retrieval

Several methods for retrieving images on the basis of color similarity have been described in the literature, but most are variations on the same basic idea. Each image added to the collection is analyzed to compute a color histogram, which shows the proportion of pixels of each color with in the image. The color histogram for each image is then stored in the database.

Texture Retrieval

The ability to retrieve images on the basis of texture similarity may not see very useful. But the ability to match on texture similarity can often be useful in distinguishing between areas of images with similar color. The best established rely on comparing values of what are known as second order statistics calculated from query and stores images.

Shape Retrieval

The ability to retrieve by shape is perhaps the most obvious requirement at the primitive level. Unlike texture, shape is a fairly well defined concept and there is considerable evidence that natural object are primarily recognized by the shape. A number of features characteristics of object shape are computed for every object identified with in each stored image. Queries are then answered by computing the same set of features for the query image, and retrieving those stored images whose features most closely match those of the query.