Content-Based Image Retrieval Based On Key Point Matching Using Scale Invariant Features Transform(Sift) Algorithm

Advances in data storage, data transmission, and image acquisition led to the creation of large image datasets. It became the motivation for systems that are able to efficiently retrieve images from these collections. This task has been addressed by the so-called Content-Based Image Retrieval (CBIR) systems. There are some quite powerful image descriptors designed to represent global features of images. These approaches have been widely used in image retrieval due to their usually low computational costs and acceptable effectiveness. However, such CBIR solutions fail on capturing some local features representing the details and nuances of the scenes. Many techniques in image processing and computer vision can capture these scene semantics. Among them, the Scale Invariant Features Transform (SIFT) has been widely used in a lot of applications. This approach relies on the choice of several parameters which directly impact its effectiveness when applied to retrieve images. Keyword CBIR SIFTSURFetc