ABSTRACT
The size of image acquisition, storage of data, and image databases are all growing. It used to take a lot of time to retrieve image because they required hand annotation and written description. The urgent need is for effective systems known as content-based image retrieval methods to manage the vast datasets. In this instance, an image's visual components, such as the shape, arrangements, and color of the objects, are taken into account in addition to the image's associated data. These suggested approaches are quicker and more effective than other traditional methods of image retrieval. In this research, we provide a novel approach in which 2-D DWT, which has been further refined, is used to extract features. The ANFIS classification machine learning algorithm is ultimately used to perform the accurate categorization. The suggested method is evaluated using a number of parameters, and the findings demonstrate that the 2-D DWT feature extraction and ANFIS classification algorithm produce better outcomes than other traditional algorithms.
