ImageDataset2Vec: An image dataset embedding for algorithm selection

2021
Convolutional Neural Networks (CNNs) have become the main solution for image classification tasks in different applications. Although several CNN architectures are available, there is no best architecture regardless the problem at hand. The selection of the most suitable CNN architecture is usually performed by trial and error, which may take much time and computational resources. Meta-learning (MtL) is a framework developed in machine learning to perform algorithm selection based on the meta-features of each task being solved. Such meta-features are usually descriptive characteristics extracted from the training dataset available in the task at hand. Despite the increasing attention of MtL for algorithm selection, its success strongly depends on defining relevant meta-features to represent the classification tasks of interest. This paper proposes the ImageDataset2Vec method for extracting meta-features to describe image classification datasets. ImageDataset2Vec adopts a pretrained deep neural network to extract features from images datasets, embedding them in a single feature vector. The derived meta-features are employed by MtL to select CNN architectures for image classification. The proposed approach was evaluated for selecting among six CNN algorithms in 45 two-classes image datasets. The results showed that MtL using ImageDataset2Vec overcame different baseline methods, selecting the best possible CNN algorithm in 84.45% of the datasets. Furthermore, the proposal was statistically equivalent to the ground truth when the best CNN is recommended, i.e., when MtL does not select the best CNN, it selects a competitive algorithm. These results show that the proposal was able to extract representative features from image datasets.
EXPERT SYSTEMS WITH APPLICATIONS
卷号:180
ISSN:0957-4174
收录类型
SSCI
发表日期
2021
学科领域
循证管理学
国家
巴西
语种
英语
DOI
10.1016/j.eswa.2021.115053
其他关键词
CLASSIFIERS
EISSN
1873-6793
被引频次(WOS)
1
被引更新日期
2022-01
来源机构
Universidade Federal Rural de Pernambuco (UFRPE) Universidade Federal de Pernambuco
关键词
Dataset embedding Algorithm selection Meta-learning Image dataset description