Using Texture in Image Similarity and Retrieval

Texture has been one of the most popular representations in image retrieval. Our image database retrieval system uses two sets of textural features, first one being the line-angle-ratio statistics which is a texture histogram computed from the properties of the surroundings and the spatial relationships of intersecting lines, second one being the variances of gray level spatial dependencies computed from co-occurrence matrices. This paper also discusses a line selection algorithm to eliminate insignificant lines and statistical feature selection methods to select the best performing subset of features. Average precision is used to evaluate the retrieval performance in comparative tests with three other texture analysis algorithms. Results show that our method is fast and effective with an average precision of 0.73 when 12 images are retrieved.