Spatial Semantic Images with Relationship Contents by Using Convolutional Neural Network and Support Vector Machine

被引：1

作者：

Chinpanthana, Nutchanun ^{[1
]}

Phiasai, Tejtasin ^{[2
]}

机构：

[1] Dhurakij Pundit Univ, Coll Innovat Technol & Engn, 110-1-4 Prachachuen Rd Laksi, Bangkok 10210, Thailand

[2] King Mongkuts Univ Technol Thonburi, 126 Pracha Uthit Rd, Bangkok 10140, Thailand

来源：

PROCEEDINGS OF 2018 VII INTERNATIONAL CONFERENCE ON NETWORK, COMMUNICATION AND COMPUTING (ICNCC 2018) | 2018年

关键词：

Image processing; image classification; convolutional neural network; support vector machine;

D O I：

10.1145/3301326.3301350

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

In recently, semantic image is an active problem in the digital image processing field. A large number of new techniques and systems have researcher involved and attempted to improve the problems. The most of techniques is done by keyword searching model. Therefore, we propose a new approach to classify the relationships between object and action. The approach is composed of three main phases: (1) data preprocessing, (2) relationship between contents, and (3) measurement and evaluation. We train and test our model on a largescale image dataset of actions. The major information contents use the relationships between object and action. The results indicated that the proposed method offers significant performance improvements in semantic classification with a maximum success rate of 80.9%.

引用

页码：267 / 272

页数：6

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