Evaluating the influence of image modifications upon content-based multimedia retrieval

被引：0
|
作者：
Tosic, Tamara [1 ]
Trpovski, Zeljen [2 ]
机构：
[1] Fac Tech Sci, Novi Sad, Serbia
[2] Univ Novi Sad, Fac Tech Sci, Dept Telecommun, YU-21000 Novi Sad, Serbia
来源：
NEUREL 2006: EIGHT SEMINAR ON NEURAL NETWORK APPLICATIONS IN ELECTRICAL ENGINEERING, PROCEEDINGS | 2006年
关键词：
CANN feature; image retrieval; MUVIS; GLCM feature; YUV feature;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, evaluation of properties of MUVIS software package is presented. Images are modified in several ways (e.g. blurred, noise added, etc.) and added into the existing MUVIS databases. Simulations show that a satisfactory retrieval performance can be obtained from small set of extracted features (in comparison with large default set), even when significantly different number of images in databases are observed. YUV feature (color), GLCM (texture) and CANN (shape) feature are extracted from images. Applied image modifications have greatest impact on extracted YUV feature (color), which differs most in comparison with original image, while the lowest impact is observed on GLCM feature (Gray, Level Co-occurrence Matrix), which shows that texture was not significantly changed during image modifications. CANN feature (shape edges extraction feature) is only slightly different from and original image. These results are similar for databases is with numbers of elements.
引用
收藏
页码：17 / +
页数：2
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