Image caption automatic generation method based on weighted feature

For people to use numerous images effectively on the web, technologies must be able to explain image contents and must be capable of searching for data that users need. Moreover, images must be described with natural sentences based not only on the names of objects contained in an image but also on their mutual relations. We propose a novel system which generates sentential annotations for general images. Firstly, a weighted feature clustering algorithm is employed on the semantic concept clusters of the image regions. For a given cluster, we determine relevant features based on their statistical distribution and assign greater weights to relevant features as compared to less relevant features. In this way the computing of clustering algorithm can avoid dominated by trivial relevant or irrelevant features. Then, the relationship between clustering regions and semantic concepts is established according to the labeled images in the training set. Under the condition of the new unlabeled image regions, we calculate the conditional probability of each semantic keyword and annotate the new images with maximal conditional probability. Experiments on the Corel image set show the effectiveness of the new algorithm.