摘要
各国政府和警方目前使用的传统淫秽图像检测方法主要以皮肤检测结果为基础,提取低层特征进行判断,在获得高正检率的同时也导致了大量的误检。为此,本文的方法从获得更直观和高层的语义知识着手,首先检测图像中局部形态变化突出的位置,并建立关于该区域形态的SIFT描述向量。把这些描述向量抽象地看作视觉“单词”,并收集淫秽图像中常见的单词。依据图像中单词出现的情况,来检测是否包含淫秽成分。结合传统检测方法,仅使用简单的Bayes规则判断,在不降低正检率的前提下,使非淫秽图像的误检率得到显著的下降。
Most pornographic image detecting systems are based on the results of skin detection. They extract the low level features in the image and obtain high positive detecting rate and high false positive detecting rate as well. The paper proposes to extract local features to obtain high level semantic knowledge. The system detects the salient points in the image and describes the local regioffs form around the points using SIFT descriptor. Look on these descriptors as visual words, then the pornographic information can be detected based on the words found. Only combining the Bayesian formula with the traditional detecting system, the algorithm takes from the false positive detecting rate remarkably when the true positive rate is kept as before.
出处
《刑事技术》
2007年第2期9-11,共3页
Forensic Science and Technology
基金
国家高技术研究发展计划(863计划)
关键词
淫秽图像
图像识别
SIFT描述子
语义分析
pornographic image
image recognition
SIFT descriptor
semantic analysis
