Cognition-Based Semantic Annotation for Web Images

Due to the semantic gap between low-level visual features and high-level semantic content of images, the methods for image annotation based on low-level visual features, cannot well meet the requirement of knowledge discovery from web images. Therefore, the automatic acquisition for high-level semantic content of image has become a hot research topic. The traditional image annotation methods represent images only by a few keywords, which cannot completely describe and rationally organize the high-level semantics of images, so it will lose a great deal of semantic information. Based on the different levels and different aspects of web images, we propose a new method to express and organize the high-level semantic content of web images. The method expresses the different levels semantic content of one image as a three-level network, composed of background semantic level, complementary semantic level and fine-grained semantic level. The experimental results show that our method is effective and efficient on the image annotation.