Textural features for image database retrieval

This paper presents two feature extraction methods and two decision methods to retrieve images having some section in them that is like the user input image. The features used are variances of gray level co-occurrences and line-angle-ratio statistics constituted by a 2-D histogram angles between two intersecting lines and ratio of mean gray levels inside and outside the regions spanned by those angles. The decision method involves associating with any pair of images either the class "relevant" or "irrelevant". A Gaussian classifier and nearest neighbor classifier are used. A protocol that translates a frame throughout every image to automatically define for any pair of images whether they are in the relevance class or the irrelevance class is discussed. Experiments on a database of 300 gray scale images with 9,600 groundtruth image pairs showed that the classifier assigned 80% of the image pairs we were sure were relevant, to the relevance class correctly The actual retrieval accuracy is greater than this lower bound of 80%.