색상과 에지 특징을 포함하는 새로운 이미지 블록 기술에 근거한 CBIR

Abstract

The growth of multimedia information has been enormous recently, especially with the advent of the Internet. A huge digital image archive is made up of millions of images, photos created by hospitals, governments, companies and academic organizations. Originally, searching and retrieving images was based on the keywords of images. In the early 1990s, Content-based Image Retrieval (CBIR) was proposed to overcome the limitations of Text-based Image Retrieval. It is a typical task of computer vision. In CBIR, images in a database are indexed using their own primitive visual features instead of human annotations such as shapes, colors, and textures. The use of different visual features is also a criterion to categorize a CBIR system. Since the visual features of an image are only based on the image itself, there is no problem of subjectivity. In this paper a CBIR algorithm which was based on image block method that combined both color and edge feature was proposed. In consideration of the main drawback of global histogram representation is dependent of the color without spatial or shape information, a new image block method that divided the image to 8 related blocks which contained more information of the image is utilized to extract image feature. Based on these 8 blocks, histogram equalization and edge detection techniques are also used for image retrieval. The experimental results show that the proposed image block method has better ability of characterizing the image contents than traditional block method and could perform retrieval system efficiently. The simulation was performed using MATLAB 7.0.