Research On Automatic Annotation Of Image Semantics

With the development of web-technique, multi-media, database-technique and unceasing popularity of the Net, the using of image is more and more popular, and the requirements for multi-media data such as graphs and images are more and more intense. The semantics-based image retrieval can not only be convenient for users, but also deliver their intentions exactly, so it is the inevitable way of image retrieval. The annotations, which are able to reduce the gap between high-level semantics and low-level visual content, can well express the semantic content of images, so the automatic annotation of image semantics is being paid more and more attention.This paper has a further exploration and study of visual feature extraction depending on analyzing correlative technology of the automatic annotation. According to the HSV(Hue, Saturation, Intensity) color space, the work of color feature extraction is finished, the process is as follows: quantifying the color space in non-equal intervals, constructing one dimension feature vector and representing the color feature by cumulative histogram. Similarly, the work of texture feature extraction is obtained by using co-occurrence matrix or frequency analysis based on wavelet transform depending on different characteristics of images. Depending on the former algorithms, image retrieval based on multi-feature fusion is achieved.Fusing different image features and using different similarity measures depending on different characteristics improves the accuracy of image retrieval. At last, on the basis of retrieval results, an example-based method is introduced to annotate images automatically. The training data are stored as the annotation experiences. In order to annotate a new input image, visual similar images are retrieved from the database. Annotation words can be derived from imitating the annotation examples of the retrieved images.A large number of simulations show that the semantic annotation of image semantics is not only able to change visual features of images into annotations, which are very useful for semantics-based image retrieval, but also overcomes the shortcomings of manual annotation, which is time-consuming and strenuous, and provides users with great convenience.