基于类别空间多示例学习的色情图像过滤算法

Pornography Filtering Algorithm Based on Classification Space Multi-instance Learning

针对传统的不良图像自动过滤算法难以适用于复杂互联网环境的问题,提出一种通过构建类别空间进行多示例学习实现图像过滤的新算法.首先在YCgCr空间中扩展Hessian矩阵检测特征点作为图像的示例,然后定义YCgCr-LBP算子作为图像示例描述符,最后基于包示例频率统计原理提出类别空间模型,并利用余弦相似度完成图像识别.利用不同成分的数据集进行了多组实验对比,结果表明,所提出的算法克服了传统依靠皮肤比例方法对皮肤或类皮肤比例较大图像识别准确度较低的问题,同时也较一般的多示例学习方法对图像具有更好的描述能力,取得了较好的实验结果,具有实际应用价值.

In order to solve the problem that the traditional pornography filtering algorithms are hardly to be used for the complex Internet environment,a novel filtering algorithm was presented based on multi-instance learning by building classification space.Firstly,Hessian matrix was used in YCgCr space to detect image feature points which are instances of the image,and then LBP operator was expanded to YCgCr space.Secondly,YCgCr-LBP operator was constructed to describe the image instances.Finally,classification space model based on frequency statistical theory was proposed,and cosine similarity was used to complete image recognition.Different data sets were used to make comparison.The results showed that using the proposed method,the accuracy is increased compared with the large skin contented images filtering by the conventional skin proportional method,and the description of the proposed method is improved compared with the general multi-instance learning method.What＇s more,better experimental results were obtained,which indicated the practical value.