Automatic image annotation is a significant and challenging problem in pattern recognition and computer vision areas. At present, existing models can not describe the visual representations of corresponding keywords, which would lead to a great number of irrelevant annotations in final annotation results. These annotation words are not related to any part of images in visual contents. A new automatic image annotation model (VKRAM) based on relevant visual keywords is proposed to overcome the above problems. Our model divides each keyword into two categories: word or non-word. Firstly, we establish visual keyword seeds of each non-word, and then a new method is proposed to extract visual keyword collections by using corresponding visual seeds. Secondly, according to the traits of words, an algorithm based on subtraction regions is proposed to extract visual keyword seeds and corresponding collections of each word. Thirdly, we propose an adaptive parameters method and a fast solution algorithm to determine the similarity thresholds of each keyword. Finally, the combinations of the above methods are used to improve annotation performance. Experimental results conducted on Corel 5K datasets verify the effectiveness of the proposed annotation image model and it has improved the annotation results on most evaluation methods.%缓冲区溢出目前已成为最常见的软件安全漏洞之一,从源代码形式来看,常见的缓冲区溢出漏 洞主要有两种类型:数据拷贝和格式化字符串造成的缓冲区溢出.分析了常见缓冲区溢出漏洞发生的原因,给出了格式化字符串存储长度的计算方法,介绍了一种基于源代码静态分析的缓冲区溢出检测算法,该算法首先对源代码进行建模,构造其抽象语法树、符号表、控制流图、函数调用图,在此基础上运用区间运算技术来分析和计算程序变量及表达式的取值范围,并在函数间分析中引入函数摘要来代替实际的函数调用.最后使用该方法对开源软件项目进行检测,结果表明该方法能够有效地、精确地检测缓冲区溢出.
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