Research On Fuzzy Uncertainty Problems Of Semantic-based Video Retrieval

Over the last decade, with the development at full speed of computer technology, network communication and multimedia technology, there are enormous changes for visual information processing theory, method, and application mode. Content-base video retrieval has become one active area of multimedia technical research and application. The semantic of video content contains a large number of conceptual and subjective components, but the digitization modality of the video image does not reflect its meaning of content concretely and ocularly. Some essential processing procedure, such as video semantic information extraction, understanding and retrieval, is to show diversity and fuzziness characteristics. Uncertainty is still a key difficult problem that cannot be avoided in the content-based video semantic retrieval. While to build the relationship between uncertainty and certainty, and to realize the mapping between video semantic features and visual features, researcher still has to face many challenges.The main achievements of this dissertation are summarized as follows:(1) Multi-category fuzzy Support Vector Machines method based on the rough set attribute reductionVideo semantic extraction process requires the use of low-level image feature and a priori constraints for intelligent classification and identification. Various interference factors in video images will lead to inaccurate classification. On the other hand, there were not only the noise samples and isolated samples in classifier training sets, there was also redundant data that is useless for identifying classification boundary. To solve this problem, based on the analysis and study mathematical characteristics of fuzzy linearly classification, nearly fuzzy linear classification and fuzzy nonlinear classification fuzzy support vector classification, classification of function model is established and membership function is constructed. The training set which is processed by attribute reduction, reduce the interference of the noise and isolated points on the classification, reduce the classifier training time and improve the classification accuracy. Experiments show that training time is reduced by an average of4.4%-34%, and that the classification accuracy increased by0.5%～7.65%(2) The possibility measure and necessity measure based on quantitative method of fuzzy reasoning During the process of understanding and establishment video semantic concept, conflicts and inconsistencies between complicated reasoning rules have to face and solve. These rules are more clues and more complex features of the semantic classification. To solve this problem this dissertation is to define and describe the quantitative of uncertainty reasoning, to prove and deduce theoretically related properties.(3) WordNet-based image semantic similarity measureVideo image semantic in different particle size, different levels of abstraction, can contain various kinds of semantic relations. These relationships mainly include local and the overall relationship, upper and lower level relations, synonymous relation, etc. When using keyword-based semantic matching, keyword itself is difficult to be directly reflect the various relations between concepts. To solve this problem, This dissertation presents the method based on the relationship between lexical semantics to organize annotation keywords and retrieve keywords. Because there is only one path between two nodes in the semantic concept tree, path length between two concepts can be used as a measure of semantic similarity. Therefore, the measure of semantic similarity between two images can convert the measure of path length between two concepts in WordNet tree.(4) The video latent semantic correlation analysis methodBetween video clips, between the various levels of semantic structure, there are varieties of correlations. This may be obstruct the retrieval result, so it need to eliminate the redundant correlation and to retain the essential semantic content.To solve this problem, this dissertation presents the method that builds the space of video feature dictionary. Using video feature dictionary to describe the structure of video content, then latent correlation vectors were removed, and the essential feature of video contents were retained. As a result, correlation interferences were eliminated. Experiments show that this method was70.59%better than the Contrast algorithm for the retrieval result of semantic items, and was17.65%equal to the Contrast algorithm for them.