The document presents an unsupervised cluster-based image retrieval algorithm that utilizes relevance feedback to enhance content-based image retrieval (CBIR) systems, primarily focusing on color, texture, and shape features of images. The authors describe feature extraction techniques and propose a k-means clustering approach to improve the efficiency and accuracy of image retrieval processes. The results suggest that the algorithm successfully clusters images based on their content, leading to effective retrieval outcomes.