Semi-supervised learning for refining image annotation based on random walk model
A unified framework for refining annotation by fusing GMM with random walk. A semi-supervised learning is used to enhance the quality of training data. Gaussian mixture model is learned to estimate initial annotations. Integrating label and visual similarities of images associated with the labels. Random walk is implemented on graph to further mine the semantic correlation.
semi-supervised learning random walk gaussian mixture model automatic image annotation expectation-maximization image retrieval