Cloud Concept Similarity Measure And Its Application In Digital Watermarking

Uncertain knowledge representation and processing is becoming a hot issue to be solved in the network computing, including qualitative and quantitative conversion, soft computing, variable granular computing and so on. Bidirectional cognition calculated model (cloud model) is that Academician Li D.Y described the randomness, fuzziness and relevance of concept on randomly determined degree by the given sample points, which based on both of the probability theory and the fuzzy mathematical theory. Uncertain knowledge representation and processing is widely used in the field of artificial intelligence, data mining, machine learning. When using the cloud model, for the quantitative data, we often convert it to the qualitative concept by cloud method, then characterize the similarity of data with the similarity measure of qualitative concept. Analyzing the similarity measure of cloud concept plays an important role, especially in the cloud digital watermarking. For example, after extracting the watermark cloud droplets embedded into the host, it will judge the extracted cloud watermark that whether is the watermark droplets embedded into the host or not. The quality of cloud concept similarity measure algorithm affects the efficiency and accuracy in the use of cloud theory,. There is some theoretical value and practical significance in studying the similarity of cloud concept,. It’s also the extended research of the cloud theory.The main content in this paper includes the following aspects:1) Describe the research background and significance, as well present the related knowledge of the uncertainty calculate and bidirectional cognition calculated model (cloud model).2) A variety of normal cloud concept similarity measure algorithms have been proposed. Analyzing the existing cloud concept similarity measure algorithms, there are the following deficiencies:high time complexity, exigent requirements of digital eigen values, worse extension to high order, low discrimination and so on. According to the existing theory, this chapter promotes the definition of the expectation curve, and derives range formula between each order expectation curve of cloud concept. Then it gains the similarity values of two normal cloud concepts by weighting the average distance. This paper desires a cloud concept similarity measure algorithm based on multi-order expectation curve weighted.3) The uncertainty mathematical model is introduced into the field of digital watermarking. The concerns focus on the generation and extraction of the watermark, and how to verify the presence of the cloud watermark. The digital eigen values of the second-order normal cloud as the cipher code, by means of the forward cloud transformation that generated one-dimensional normal cloud watermark. Vector image block is transformed by DCT transformation in the transform domain, then the watermark cloud droplets are embedded into the middle frequency coefficients through modulating the middle frequency coefficients. It analyzes the error of the watermarks that are extracted from the vector image with cloud watermark by the different reverse cloud transformation algorithm. In the attacking robustness experiment, the similarity measure algorithm in this paper is compared with the original one-dimensional normal cloud watermark algorithm. The experiment results indicate that introducing the correlation theory of the uncertainty cloud model theory to the field of digital watermarking is feasible. Its advantage is that part of the watermark cloud droplets can be better resorted to the three parameters of the original cloud concept.