标题:Grading image retrieval based on DCT and DWT compressed domains using low-level features
作者:Wang, Chengyou ;Zhang, Xinyue ;Shan, Rongyang ;Zhou, Xiao
作者机构:[Wang, Chengyou ;Zhang, Xinyue ;Shan, Rongyang ;Zhou, Xiao ] School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 更多
通讯作者:Wang, Chengyou
来源:Journal of Communications
出版年:2015
卷:10
期:1
页码:64-73
DOI:10.12720/jcm.10.1.64-73
关键词:Color features; Compressed domain; Content based image retrieval (CBIR); Discrete cosine transform (DCT); Discrete wavelet transform (DWT); Texture features
摘要:Nowadays, the majority of images are in JPEG and MPEG compressed formats, and JPEG2000 is considered to be the next generation of compression standard due to the high-performance of discrete wavelet transform (DWT). It is time-consuming and occupies too much memory in conventional image retrieval ways. In order to solve these problems, we use grading retrieval techniques to implement image retrieval based on discrete cosine transform (DCT) compressed domain and DWT compressed domain. For image retrieval based on DCT domain, we use color features: color moment and color histogram, to describe content of images and propose a new dynamic color space quantization based on color distribution; For image retrieval based on DWT domain, we use texture features as two level feature vectors. The mean and standard deviation of low frequency sub-band coefficients are used as the first level retrieval. The means and standard deviations of selected high frequency sub-band coefficients are used as the second level retrieval. Furthermore, the third level retrieval is achieved by the fast wavelet histogram. Our experiment results clearly show that the two grading image retrieval algorithms work better than other algorithms: store memory is reduced and retrieval accuracy is improved. © 2015 Engineering and Technology Publishing.
收录类别:EI;SCOPUS
Scopus被引频次:6
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84922014191&doi=10.12720%2fjcm.10.1.64-73&partnerID=40&md5=c49c4f47b7c4f484d7b001646c5e81d9
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