CBIR-ANR: A content-based image retrieval with accuracy noise reduction

被引：2
|
作者：
Vieira, Gabriel S. [1 ]
Fonseca, Afonso U. [2 ]
Soares, Fabrizzio [2 ]
机构：
[1] Fed Inst Goiano, Comp Vis Lab, BR-75790000 Urutai, GO, Brazil
[2] Univ Fed Goias, Inst Informat, BR-74690900 Goiania, GO, Brazil
来源：
SOFTWARE IMPACTS | 2023年 / 15卷
关键词：
Image retrieval; Image descriptor; Microstructures; Features fusion; Local binary pattern; Low-level features combination; DESCRIPTOR;
D O I：
10.1016/j.simpa.2023.100486
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
Due to the expansion of multimedia data leveraged by social networks and smartphone devices, large sets of information are available and increasing daily. In this context, information retrieval is crucial to open new opportunities to individuals, governments, and businesses. Therefore, we present the CBIR-ANR software in which the content-based image retrieval (CBIR) is followed by an accuracy noise reduction (ANR) strategy that adjusts query responses and increases assertiveness in image retrieval. Also, the software combines three low-level features to form a 187-dimensional feature vector, which is size efficient for large-scale data sets and competitive with related work.
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收藏
页数：7
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