图像挖掘:一种基于纹理的数据挖掘新方法

M. Sahu, M. Shrivastava, M. Rizvi
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引用次数: 21

摘要

图像数据挖掘可以通过对数据进行切片和切块来手动完成,直到模式变得明显。或者,它可以通过自动分析数据的程序来完成。在基于内容的图像检索(CBIR)系统中,图像的颜色、纹理和形状是原始图像描述符。图像的基本特征,用于从图像数据库中识别和检索紧密匹配的图像。由于图像数据库非常庞大,手动从图像数据库中提取图像非常困难。本文提出了一种新的图像纹理信息检索框架,实现了比图像形状特征更高的检索效率。在准确性和计算成本之间存在权衡。使用更有效的算法来解决问题,提高了计算能力,降低了整个系统的成本,从而减少了权衡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Image Mining: A New Approach for Data Mining Based on Texture
Image data mining can be done manually by slicing and dicing the data until a pattern becomes obvious. Or, it can be done with programs that analyse the data automatically. Colour, texture and shape of an image have been primitive image descriptors in Content Based Image Retrieval (CBIR) system. Primitive features of an image used to identify and retrieve closely matched images from an image database. It is very difficult to extract images manually from image database because they are very large. This paper presents a novel framework for texture information of an image and achieves higher retrieval efficiency than the shape features of an image. There is a trade-off between accuracy and computational cost. The trade-off decreases as more efficient algorithm is used to solve the problem and increases the computational power and will decreases the cost of the whole system as well.
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