A fast image classification algorithm using Support Vector Machine

With the Internet technology development and the popularization of networks, multimedia information, especially images and visual information because of its rich and varied information, has become an important part of information retrieval, in which video information which is the image information as a basis. In the image retrieval technology, in order to retrieve the results closer to people´s thinking, how to use semantic-based content-based image retrieval using low-level features of the image fit the human to become a key high-level semantics. Support Vector Machine is considered a highly effective performance of the classifier are superior in many occasions can be applied. On this basis, direct push support vector machine (TSVM) is a combination of support vector machine algorithm, to achieve an efficient classification algorithm. This paper puts forward a new algorithm in the original TSVM algorithm based on the use of labeled samples at the same time, consider a sample of the classifier without labeling effects, and add in the original part of the screening process conditions, as well as process improvement, making the new algorithm in time complexity have significantly decreased, while no significant effect on algorithm results.