Using Co-occurrence and Granulometry Features for Content Based Image Retrieval

被引：1
|
作者：
Said, Lal [1 ]
Khurshid, Khurram [1 ]
Aman, Asia [1 ]
机构：
[1] Inst Space Technol, Dept Elect Engn, Islamabad 44000, Pakistan
来源：
EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS | 2018年 / 4卷 / 16期
关键词：
Granulometry Features; CCF; Content Based Image Retrieval;
D O I：
10.4108/eai.13-4-2018.154479
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This communication presents a novel system for Content Based Image Retrieval (CBIR) using Granulometry and Color Co-occurrence Features (CCF). These features are extracted directly from images using visual codebook. Relative distance measures are used to identify the similarity between the stored images and the query image. Results show that proposed method of using Granulometry and CCF is superior to most state of the art CBIR systems. The proposed system is tested on Wang image database that contains 1000 images having different categories. The performance of the system, quantified using the Average Precision Rate (APR), is very encouraging.
引用
收藏
页码：1 / 5
页数：5
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