An Effective Web Image Searching Engine Based on SIFT Feature Matching

Abstract

This paper provides a Web content-based image searching engine based on SIFT (Scale Invariant Feature Transform) feature matching. SIFT descriptors, which are invariant to image scaling and transformation and rotation, and partially invariant to illumination changes and affine, present the local features of an image. Therefore, feature keypoints can be extracted more accurately by using SIFT than color, texture, shape and spatial relations feature. To decrease unavailable features matching, a dynamic probability function replaces the original fixed value to determine the similarity distance and database from training images. Then, by using pretreatment of the source images, the keypoints will be stored to the XMI format, which can improve the searching performance. Finally, the results displayed to the user through the HTML The experimental results show that this method improves the stability and precision of image searching engine.