Supervised Semantic Image Annotation Using Region Relevance
Fei Shi, Fangfang Yang, Jiajun Wang · Physics Procedia · 2012
Automatic image annotation is the key to semantic-based image retrieval. In this paper we formulate image annotation as a multi-class classification problem, which deals with the weak annotation problem and works with image-level ground truth training data. The relationship between low-level visual features and semantic concepts is found by supervised Bayesian learning. For each region in the test image, a posterior probability for each concept is calculated from class densities estimated from the training set and then the probability is modified using relevance with the other regions in the image. The image-level posterior probabilities are obtained by combining the regional posterior probabilities and keywords are selected according to their ranks. The proposed algorithm achieves good annotation performance on the Corel5K benchmark dataset.