Background scene classification robust to the influence of human regions

R. Mase, R. Oami, T. Nomura
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Abstract

We propose a background scene classification method robust to the influence of human regions. Conventional methods classify scene of an image by using image features extracted from entire region in the image. Therefore, in these methods, the influence of the human region such as color of the skin and the clothes reduces classification accuracy of the background scene. Our method classifies background scene of an image by using image features extracted from only background region except detected human regions. The experimental results show that the proposed method improves average of the rate at the balance point between recall rate and precision rate in almost all background scenes compared to the conventional method.
背景场景分类对人类区域的影响具有鲁棒性
提出了一种对人类区域影响具有鲁棒性的背景场景分类方法。传统方法是利用图像中整个区域提取的图像特征对图像进行场景分类。因此,在这些方法中,皮肤颜色、衣服等人体区域的影响会降低背景场景的分类精度。该方法只提取背景区域的图像特征,而不提取检测到的人体区域,对图像的背景场景进行分类。实验结果表明,与传统方法相比,该方法在几乎所有背景场景下都提高了查全率和查准率平衡点的平均值。
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