Segmentation-based Fractal Texture Analysis and Color Layout Descriptor for Content Based Image Retrieval

Due to the information technology which is rapidly developing, digital content is becoming increasingly difficult to handle. This include images that are kept on digital cameras, CCTV and medical scanners. Areas such as medical and forensic science are using these databases to do critical tasks which include diagnosing of diseases or identification of criminal suspects. However, to manage and search the similar images from these databases are not an easy task. Content Based Image Retrieval (CBIR) is one of the techniques used to manage and search similar images from a database. The performance of CBIR depends on the low level (Texture, Color and Shape) features. In this paper, a new feature vector to represent the image in terms of low level features and to improve the performance of CBIR is proposed. The proposed approach used texture and color feature namely SFTA-CLD. SFTA-CLD is based on Segmentation-based Fractal Texture Analysis (SFTA) and Color Layout Descriptor (CLD). SFTA-CLD is assessed using Coral image gallery and validated by comparing the performance in terms of average precision with previous CBIR techniques.