Mining of Semantic Image Content Using Collective Web Intelligence

Human users spend a vast amount of time in interacting with image contents on the Web. Their interaction entails the exercise of considerable perceptive intelligence, visual judgment and mental evaluation. For high-level semantic image features and concepts, such processes of intelligent judgment cannot be mechanized or carried out automatically by machines. In this chapter, an indexing method is described whereby the aggregate intelligence of different Web users is continuously transferred to the Web. Such intelligence is codified, reinforced, distilled and shared among users so as to enable the systematic mining and discovery of semantic image contents. This method allows the collaborative creation of image indexes, which is able to instill and propagate deep knowledge and collective wisdom into the Web concerning the advanced semantic characteristics of Web images. This method is robust and adaptive, and is able to respond dynamically to changing usage patterns caused by community trends and social networking. © Springer-Verlag London Limited 2010.Human users spend a vast amount of time in interacting with image contents on the Web. Their interaction entails the exercise of considerable perceptive intelligence, visual judgment and mental evaluation. For high-level semantic image features and concepts, such processes of intelligent judgment cannot be mechanized or carried out automatically by machines. In this chapter, an indexing method is described whereby the aggregate intelligence of different Web users is continuously transferred to the Web. Such intelligence is codified, reinforced, distilled and shared among users so as to enable the systematic mining and discovery of semantic image contents. This method allows the collaborative creation of image indexes, which is able to instill and propagate deep knowledge and collective wisdom into the Web concerning the advanced semantic characteristics of Web images. This method is robust and adaptive, and is able to respond dynamically to changing usage patterns caused by community trends and social networking. © Springer-Verlag London Limited 2010.