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A fast image classification algorithm using Support Vector Machine

机译：一种快速图像分类算法使用支持向量机

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摘要

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.
机译：通过互联网技术的开发和网络的普及，多媒体信息，尤其是图像和视觉信息，因为其丰富和各种信息，已经成为信息检索的重要部分，其中作为基础的视频信息。在图像检索技术中，为了将结果更接近人们的思考，如何使用基于语义的基于内容的图像检索使用的图像的图像符合人们成为一个关键的高级语义。支持向量机被认为是分类器的高效性能在很多场合都可以应用。在此基础上，直接推送向量机（TSVM）是支持向量机算法的组合，实现了高效的分类算法。本文在原始TSVM算法上提出了一种新的算法，同时考虑使用标记的样本的使用，考虑分类器的样本而无需标记效果，并在筛选过程条件的原始部分以及过程中添加改进，使新的时间算法在时间复杂性显着降低，而对算法结果没有显着影响。

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