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首页> 外文会议>2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics >Pornographic image classification based on top down color-saliency based BoW representation
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Pornographic image classification based on top down color-saliency based BoW representation

机译：基于自上而下基于颜色显着性的BoW表示的色情图像分类

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摘要

Since color is an important visual clue of the pornographic image, this study presents a new framework for pornographic image classification based on the fusion of color and shape information for the bag of words representation. This framework contains three fusion patterns: The early fusion, late fusion and top down color-saliency based fusion, which are compared intensively. Based on the comparison, the top down color-saliency fusion based pornographic image classification method is proposed by using the statistical class prior of each color word to weight the shape word. In the late fusion and color-saliency based fusion, color name is adopt to represent the color information. To verify the effectiveness of spatial constrain on the words, we also compared the shape features quantized by vector quantization and locality-constrained linear coding. The experimental results show that our model combines the shape and color information properly and it is superior over the popular methods to distinguish the normal and pornographic-like images from the pornographic ones.
机译：由于颜色是色情图片的重要视觉线索，因此本研究提出了一种基于颜色和形状信息融合的色情图片分类新框架。此框架包含三种融合模式：早期融合，晚期融合和基于自上而下的颜色显着性的融合，它们进行了比较。在此基础上，提出了一种基于自上而下的色彩显着性融合的色情图像分类方法。在后期融合和基于颜色显着性的融合中，采用颜色名称来表示颜色信息。为了验证对单词进行空间约束的有效性，我们还比较了通过矢量量化和局部约束线性编码量化的形状特征。实验结果表明，我们的模型正确地结合了形状和颜色信息，并且比流行的方法更好地区分了正常图像和色情图像与色情图像。

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