Cognition-based semantic annotation for web images

Publication Type:
Conference Proceeding
Citation:
Proceedings - 4th IEEE International Conference on Big Data and Cloud Computing, BDCloud 2014 with the 7th IEEE International Conference on Social Computing and Networking, SocialCom 2014 and the 4th International Conference on Sustainable Computing and Communications, SustainCom 2014, 2015, pp. 540 - 546
Issue Date:
2015-01-01
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© 2014 IEEE. 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.
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