SPIE Journal Paper | 11 August 2022
KEYWORDS: Image classification, Image retrieval, Databases, Quantum computing, Image processing, Feature extraction, Image enhancement, Image fusion, Detection and tracking algorithms, Buildings
To further improve the performance of image retrieval, we propose an image two-classification algorithm based on continuous-time quantum walk (CTQW) model. First, a weighted undirected complete graph is constructed based on a new weighted edge method, that is, the images in the image set are used to represent the nodes on the weighted complete graph, and every two images are connected to represent the edge on the weighted complete image, the weighted value of which represents the similarity between adjacent images. Then, the CTQW is executed to realize the image two-classification based on the quantum walk model of the weighted complete graph, that is, the CTQW is performed from two types of unrelated images, and the two-classification of images is realized that the relevant and unrelated labels are assigned to each image according to the obtained probability distribution two-classification. Finally, the experimental results show that the proposed two-classification algorithm based on CTQW has a good classification effect from the visual effect, and the evaluation indexes of image classification, such as the average precision rate, the recall rate, and the accuracy rate, are all higher than 0.85. In addition, the proposed image two-classification algorithm based on CTQW can be extended to image multiclassification if the quantum walk is performed using multiple irrelevant pictures as the starting point. Similarly, the extended image multiclassification method also has a better classification effect through the experiments and the evaluation indexes of image classification.
