We propose a new framework termed Keyblock for content-based image retrieval, which is a generalization of the text-based information retrieval technology in the image domain. In this framework, methods for extracting comprehensive image features are provided, which are based on the frequency of representative blocks, termed keyblocks, of the image database. Keyblocks, which are analogous to index terms in text document retrieval, can be constructed by exploiting the vector quantization (VQ) method which has been used for image compression. By comparing the performance of our approach with the existing techniques using color feature and wavelet texture feature, the experimental results demonstrate the effectiveness of the framework in image retrieval.
我们为基于内容的图像检索提出了一个名为 Keyblock I>的新框架,该框架是图像领域中基于文本的信息检索技术的概括。在此框架中,提供了用于提取全面图像特征的方法,这些方法基于图像数据库的代表性块(称为关键块)的出现频率。可以通过利用已经用于图像压缩的矢量量化(VQ)方法来构造类似于文本文档检索中的索引词的键块。通过将我们的方法与使用颜色特征和小波纹理特征的现有技术的性能进行比较,实验结果证明了该框架在图像检索中的有效性。 P>
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