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2019 | OriginalPaper | Chapter

Supervised Deep Learning for Hierarchical Image Data Retrieval

Authors : Been-Chian Chien, Yueh-Chia Hsu, Ya-Yu Huang

Published in: Multidisciplinary Social Networks Research

Publisher: Springer Singapore

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Abstract

The techniques of feature extraction and representation on image data have been significantly progressed in recent years due to the development of deep learning. With a large number of representative image features being extracted from ImageNet by convolutional neural networks, many object recognizing applications were successfully accomplished effectively. In this paper, two supervised image retrieval models for retrieving images with similar hierarchical concept are investigated and compared. First, image features are extracted by pre-trained VGG convolutional networks. Then, the supervised retrieval models are learned from a set of images with hierarchical concept labels. The experimental results show that the hash-based model generally is superior to classifier-based model both in F1 measure and MAP no matter what in coarse level or fine level of concept hierarchy.

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Metadata
Title
Supervised Deep Learning for Hierarchical Image Data Retrieval
Authors
Been-Chian Chien
Yueh-Chia Hsu
Ya-Yu Huang
Copyright Year
2019
Publisher
Springer Singapore
Book
Multidisciplinary Social Networks Research

Print ISBN: 978-981-15-1757-0

Electronic ISBN: 978-981-15-1758-7

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-981-15-1758-7_1

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