An image retrieval method based on SIFT feature extraction

Current image retrieval technologies primarily rely on features such as color, texture, and shape to conduct searches, but their search speed and accuracy still cannot meet user demands. In this paper, while using the Scale Invariant Feature Transform (SIFT) algorithm for image retrieval, the Locality Preserving Projection (LPP) method is employed to reduce the dimensionality of the algorithm, decreasing the number of feature points to enhance the real-time nature of feature point matching. Furthermore, an enhanced approximate nearest neighbor method is applied in the feature point matching process, incorporating a secondary verification mechanism to confirm potential point matches, thus improving matching accuracy. A set of 300 images across five different categories is selected as the target image database for experimental research on image retrieval. Experiments conducted under varying lighting and scaling conditions validate the improved real-time performance and matching accuracy of the enhanced SIFT algorithm in image retrieval, highlighting its potential utility in practical image retrieval applications.