Automatic Image Annotation and Retrieval Using Fuzzy C-Means Clustering with Gaussian Bare Bones White Shark Optimization

Image annotation is the process of assigning significant labels to specific parts of an image, making it easier for systems to categorize, interpret, and retrieve visual data.These annotations are then used in the retrieval process to search for and recover images based on tagged data, improving the accuracy and efficiency of identifying relevant images.However, inadequate or inconsistent annotations lead to poor image retrieval performance, decreasing the overall effectiveness.This research proposes the Fuzzy C-Means Clustering-Gaussian Bare Bones White Shark Optimization (FCM-GBWSO) algorithm for automatic image annotation and retrieval.In WSO, GB is incorporated to prevent premature convergence and avoid local optima issues in generating cluster centroids and clustered images.Various feature extraction techniques, such as ResNet50 and Color Moments, are applied to extract features effectively.Feature transformation and Neighbourhood Component Analysis (NCA) are then used to transform the features into similar significance and select the best features.The proposed FCM-GBWSO achieves an average precision of 0.98, 0.96, and 0.97 on the Corel-10k, Caltech 256 datasets, and Corel 1k respectively, outperforming existing methods like hierarchical clustering and Deep Neural Network-based Deep Search and Rescue (DNN-SAR).