Efficient video images retrieval by using local co-occurrence matrix texture features and normalised correlation

Since multimedia data is widely used in many fields, research is needed on how to search that data. As a method of retrieving multimedia data, especially for image data, content based image retrieval (CBIR) systems have obtained lots of interest. In order to develop CBIR systems, many different properties of an image have been considered for methods of retrieving image data such as colour, texture, shape, etc. The paper proposes a simple and efficient image retrieval algorithm, which retrieves images on the basis of subregional texture characteristics. In order to retrieve images in terms of contents, it is in general required to obtain a precise segmentation. However, it is very difficult and takes a lot of computing time for precise segmentation. Therefore, the proposed algorithm subdivides an image in terms of a certain sized windows, and finds out whether an image contains a query pattern on the basis texture features. Also, these texture features are described by only six different texture features produced from a co-occurrence matrix. For an image retrieval system, a normalised correlation is adopted as a similarity function, which is not dependent on the range of texture feature values. Finally, the proposed algorithm is applied to various images and produces competitive results.