Deep learning and t-SNE projection for plankton images clusterization

被引：0

作者：

Homsi Goulart, Antonio Jose ^{[1
]}

Morimitsu, Alexandre ^{[1
]}

Jacomassi, Renan ^{[1
]}

Hirata, Nina ^{[1
]}

Lopes, Rubens ^{[2
]}

机构：

[1] Univ Sao Paulo, Inst Math & Stat, Sao Paulo, Brazil

[2] Univ Sao Paulo, Oceanog Inst, Sao Paulo, Brazil

来源：

OCEANS 2021: SAN DIEGO - PORTO | 2021年

基金：

巴西圣保罗研究基金会;

关键词：

clusterization; dataset labelling; convolutional neural network; plankton imaging;

D O I：

暂无

中图分类号：

U6 [水路运输]; P75 [海洋工程];

学科分类号：

0814 ; 081505 ; 0824 ; 082401 ;

摘要：

In this paper we present a pipeline to cluster unlabelled image samples. Although not restricted to plankton image applications, we present the system within this context. Feature maps obtained from a deep learning architecture (DenseNet) are fed to the t-SNE projection in order to obtain 2D clusters. The method successfully creates clusters that can be used in interactive software, for quick manual classification of images batches.

引用

页数：4

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