Image Slicing and Statistical Layer Approaches for Content-Based Image Retrieval - Al-Nihoud, Jehad Qubiel Odeh (2004)
Two new approaches for colour features representation and comparison in digital images to handle various problems in the field of content-based image retrieval are proposed. The first approach is a double-layered system utilising a new technique, which is based on image slicing, combined with statistical features extracted and compared in each layer (ISSL). The images database is filtered in the first layer based on the similarities of brightness compared with the query image and ranked in the second layer, based on the similarities of the contrast values between the query image and the set of candidate images retrieved through the first layer. Although different distance measurements are available, the city block known as L1-norm distance measurement is used. This is due to its speed efficiency and accuracy. Different experiments are applied to different database sets, containing different number of images. The results show that the approach is scalable to the varying size of the database, robust, accurate, and fast. A comparison between the colour histogram approach and the proposed approach shows that the proposed system is more accurate and the speed of performance is much better. A new paradigm to choose the proper threshold value is proposed based on the autocorrelation of the distance vector. Moreover, an image retrieval system based on entropy as a visual discriminator is developed and compared with ISSL. The results show that the proposed ISSL approach is able to achieve better precision and reaches higher recall levels as compared with entropy approach.