Semi-Automatic Semantic Based Natural Images Retrieval System

被引：1

作者：

Alebiary, Doaa M. ^{[1
]}

Semary, Noura A. ^{[2
]}

Zayed, Hala H. ^{[1
]}

机构：

[1] Benha Univ, Fac Comp & Informat, Dept Comp Sci, Banha, Egypt

[2] Menoufia Univ, Fac Comp & Informat, Dept Informat Technol, Shibin Al Kawm, Egypt

来源：

INTERNATIONAL CONFERENCE ON INFORMATICS AND SYSTEMS (INFOS 2016) | 2016年

关键词：

Big Data; Content-Based Image Retrieval; High-Level Semantics; Semantic Gap;

D O I：

10.1145/2908446.2908503

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Content-based Image Retrieval (CBIR) is a term referring to looking for digital images by analyzing content of images rather than its metadata. CBIR system retrieves the image via low-level features such as color, texture and shape. In this work, we propose an improved CBIR system that retrieves images from a database based on the semantic features of them. The methodology that divide image and extracts low-level features from each region and label each one with the suitable concept (Sky, Sand, Water, trunks, foliage, rocks,..., and Grass). The results of the paper reflects the efficiency of the system for retrieving images with up to 98% recognition ratio.

引用

页码：323 / 324

页数：2

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