用于图像索引和检索的新小波特征

A. Lakshmi, S. Rakshit
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引用次数: 9

摘要

图像描述符将数据库中的图像编码为特征向量。特征向量在基于内容的图像检索中起着重要的作用。提出了一种新的基于小波的特征向量。大多数自然图像具有短跨度的高频和向大跨度延伸的低频。因此,我们的特征向量的设计是这样的,它在较高的频率下提供更高的空间定位和较低的频率分辨率,而在较低的频率下则相反。将图像在各个子带和不同空间分辨率(频带越高)下的频率含量能量存储为特征向量。因此,给定的特征向量也编码高频信息。实验结果证明了该算法相对于传统算法的优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
New wavelet features for image indexing and retrieval
Image descriptors encode the images in the database as feature vectors. Feature vectors play main role in content based image retrieval. This paper proposes a new feature vector based on wavelets. Most of the natural images have short span high frequencies and low frequencies extending for larger span. Hence, the design of our feature vector is such that it provides higher spatial localization and lower frequency resolution at higher frequencies and the reverse for lower frequencies. The energy of the frequency content of the image at various sub-bands and different spatial resolution (higher for higher frequency bands) is stored as feature vector. Thus, the given feature vector encodes high frequency information as well. The superiority of the proposed algorithm over some traditional algorithms is substantiated with results.
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