LOCAL RADIUS INDEX - A NEW TEXTURE SIMILARITY FEATURE

被引:0
|
作者
Zhai, Yuanhao [1 ]
Neuhoff, David L. [1 ]
Pappas, Thrasyvoulos N. [2 ]
机构
[1] Univ Michigan, Dept EECS, Ann Arbor, MI 48109 USA
[2] Northwestern Univ, Dept EECS, Evanston, IL USA
关键词
texture similarity; image coding; retrieval; ROTATION; SCALE;
D O I
暂无
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
We develop a new type of statistical texture image feature, called a Local Radius Index (LRI), which can be used to quantify texture similarity based on human perception. Image similarity metrics based on LRI can be applied to image compression, identical texture retrieval and other related applications. LRI extracts texture features by using simple pixel value comparisons in space domain. Better performance can be achieved when LRI is combined with complementary texture features, e.g., Local Binary Patterns (LBP) and the proposed Subband Contrast Distribution. Compared with Structural Texture Similarity Metrics (STSIM), the LRI-based metrics achieve better retrieval performance with much less computation. Applied to the recently developed structurally loss-less image coder, Matched Texture Coding, LRI enables similar performance while significantly accelerating the encoding.
引用
收藏
页码:1434 / 1438
页数:5
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