Optimized learning instance-based image retrieval

Image retrieval is a recognition technique in the field of computer vision. In most cases, high-quality retrieval is often supported by adequate learning instances. However, in the process of learning instance selection, some useless, repeated, invalid, and even mistaken learning instances are often selected. Low-quality instances not only add to the computing burden but also decrease the retrieval quality. In this study, we propose a learning instance optimization method. Initially, we classify the images into scene and object images by using the K-means clustering model. We use different methods to handle these two groups of images. For scene images, we use the Euclidean distance of the GIST descriptor to select the optimized learning instances. For object images, we use the improved spatial pyramid matching and optimal instance distance methods to select the optimized learning instances. Finally, we implement experiments using one large image database to check the effectiveness of our proposed algorithm. Results show that our method can not only improve retrieval quality but also decrease the number of learning instances.