Automatic image annotation with low-level features and conditional random fields
This work is devoted to the problem of automatic image annotation by analyzing image low-level characteristics. This problem consists in assigning words of a natural language to an arbitrary image by analyzing image low-level characteristics without any other additional information. Automatic image annotation could be useful for extraction of high-level semantic information from images, organizing huge image bases and performing search by text query. We propose the general annotation scheme consisting of the three stages. First we extract several types of the low-level features from the images. After that we compute secondary features using clustering technique. The automatic annotation is produced finally by applying Conditional Random Field to the secondary features.