Analysis of Multi-Features Combination of Unsupervised Content Based Image Retrieval with Different Degrees of Accuracy

The method of identifying related images in an image database is known as image retrieval. Text-based and content-based data processing are the two main forms of image retrieval processes. The visual characteristics of an image, such as color, texture, shape, and spatial design, are used in the content-based approach of image retrieval. In unsupervised mode, the images are retrieved using a cluster-based graph partitioning algorithm. The efficiency of different content based image retrieval systems is contrasted in this paper by fusing multiple image characteristics. The creation of four versions resulted from the integration of several features. Compute the union of all four models by normalizing the value between 0 and 1. The data comes from the COREL image database, which includes 1000 images of the same resolution. According to this article, images can be best recovered using three model-based features rather than two features. The accuracy of the union is thought to be superior.