Data Augmentation and Transfer Learning Applied to Charcoal Image Classification

被引：0

作者：

Menon, Luciana T. ^{[1
]}

Laurensi, Israel A. ^{[1
]}

Penna, Manoel C. ^{[1
]}

Oliveira, Luiz E. S. ^{[2
]}

Britto, Alceu S., Jr. ^{[1
,3
]}

机构：

[1] Pontifical Catholic Univ Parana PUCPR, Curitiba, PR, Brazil

[2] Fed Univ Parana UFPR, Curitiba, PR, Brazil

[3] State Univ Ponta Grossa UEPG, Ponta Grossa, PR, Brazil

来源：

PROCEEDINGS OF 2019 INTERNATIONAL CONFERENCE ON SYSTEMS, SIGNALS AND IMAGE PROCESSING (IWSSIP 2019) | 2019年

关键词：

data augmentation; microscopic charcoal image classification; transfer learning;

D O I：

10.1109/iwssip.2019.8787271

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 paper proposes the evaluation of data augmentation impact in the process of microscopic charcoal image classification. Two data augmentation approaches were explored, namely morphological transformations and sub-images. From the augmented data, a pre-trained Inception-v3 network was used to train a classifier of charcoal species. The best result was found through the technique of sub-images, with an average accuracy of 99.36%.

引用

页码：69 / 74

页数：6

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