Review about SIFT and Local Feature Extraction in Content Based Image Retrieval

As internet technology expands and the widespread use of digital devices, Content Based Image Retrieval CBIR has seen rapid development and application across a range of areas in computer vision and artificial intelligence Today, it's possible to retrieve related images efficiently and effectively from large scale databases using just an input image, In the last decade, there has been a significant push towards developing new CBIR theories and models, resulting in the establishment of many effective CBIR algorithms. CBIR is a crucial tool for locating images within a large dataset that share similarities with a certain query image., with the process generally involving the comparison of key features of the query image against those in the dataset to help rank and retrieve the most relevant images, the general aim of the paper is to explore the key concepts and methods central to Content Based Image Retrieval emphasizing the importance of sophisticated feature extraction techniques like SIFT,SURF,ORB, and how machine learning and deep learning are transforming the efficiency and accuracy of image retrieval, It sheds light on the significant shifts in how images are processed and retrieved, highlighting both the new opportunities and challenges emerging in the field.