SIFT-Based Image Retrieval Combining the Distance Measure of Global Image and Sub-Image
This paper presents a similarity match method based on global image and local sub-image using the SIFT features of digital images, and applies our algorithm to Content-Based Image Retrieval. In order to make the SIFT-based image retrieval results better, the most fundamental improvement comes in two areas. One is the introduction of the distance between the matched keypoints, and the shorter the distance between the matched keypoints, the lower the similarity measure. The other is that the image is partitioned off into sub-images, which reduces the mismatched keypoints. Experiments demonstrate effectiveness of the proposed approach compared with the traditional SIFT-based image retrieval and reveal it as a good option to image retrieval.