Content Base Image Retriveal Using Feature Extraction

Abstract

Now a day's people are interested in using digital images. There is a great need for developing an efficient technique for finding the images. A significant and increasingly popular approach that helps in the retrieval of image data from a huge collection is called Content Based Image Retrieval (CBIR). In order to find an image, image has to be represented with certain features. Colour, texture and shape are three important visual features of an image. We will implement an efficient image retrieval technique which uses dynamic dominant colour, texture and shape features of an image. It will be helpful and easy way to retrieve image from huge database. In order to find image from huge database which uses dominant colour it is image is uniformly divide into 8 coarse partition as a first step after above coarse partition, the centroid of each partition is selected dominant colour .Texture of an image of an image is obtain by gray level Co-Occurrence matrix(GLCM) and as per shape .There is no universal definition of what shape is either. Shape is a well-defined concept and there is considerable evidence that natural objects are primarily recognized by their shape. Thus, using matching and comparison algorithms, the colour, texture and shape features of one image are compared and matched to the corresponding features of another image. This comparison is performed using colour, texture and shape distance metrics. In the end, these metrics are performed one after another, so as to retrieve database images that are similar to the query.

 

Keywords: Digitalize, feature extraction, image feature database, image database, image matching and multidimensional indexing