research-article

A Semi-supervised Learning Approach Based on Adaptive Weighted Fusion for Automatic Image Annotation

Authors:

Zhiping ShiAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 17, Issue 1

Article No.: 37, Pages 1 - 23

https://doi.org/10.1145/3426974

Published: 16 April 2021 Publication History

Abstract

To learn a well-performed image annotation model, a large number of labeled samples are usually required. Although the unlabeled samples are readily available and abundant, it is a difficult task for humans to annotate large numbers of images manually. In this article, we propose a novel semi-supervised approach based on adaptive weighted fusion for automatic image annotation that can simultaneously utilize the labeled data and unlabeled data to improve the annotation performance. At first, two different classifiers, constructed based on support vector machine and covolutional neural network, respectively, are trained by different features extracted from the labeled data. Therefore, these two classifiers are independently represented as different feature views. Then, the corresponding features of unlabeled images are extracted and input into these two classifiers, and the semantic annotation of images can be obtained respectively. At the same time, the confidence of corresponding image annotation can be measured by an adaptive weighted fusion strategy. After that, the images and its semantic annotations with high confidence are submitted to the classifiers for retraining until a certain stop condition is reached. As a result, we can obtain a strong classifier that can make full use of unlabeled data. Finally, we conduct experiments on four datasets, namely, Corel 5K, IAPR TC12, ESP Game, and NUS-WIDE. In addition, we measure the performance of our approach with standard criteria, including precision, recall, F-measure, N+, and mAP. The experimental results show that our approach has superior performance and outperforms many state-of-the-art approaches.

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Index Terms

A Semi-supervised Learning Approach Based on Adaptive Weighted Fusion for Automatic Image Annotation
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Visual content-based indexing and retrieval
  2. Machine learning

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Information & Contributors

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Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 17, Issue 1

February 2021

392 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3453992

Editor:
Alberto Del Bimbo
University of Firenze, Italy

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Copyright © 2021 Association for Computing Machinery.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 16 April 2021

Accepted: 01 September 2020

Revised: 01 July 2020

Received: 01 February 2020

Published in TOMM Volume 17, Issue 1

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https://dl.acm.org/doi/10.1007/s10994-023-06440-8
Salar AAhmadi A(2024)Improving loss function for deep convolutional neural network applied in automatic image annotationThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-023-02873-340:3(1617-1629)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s00371-023-02873-3
Oussama AKhaldi BKherfi M(2023)A fast weighted multi-view Bayesian learning scheme with deep learning for text-based image retrieval from unlabeled galleriesMultimedia Tools and Applications10.1007/s11042-022-13788-x82:7(10795-10812)Online publication date: 1-Mar-2023
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Zhu QLi ZKuang WMa H(2023)A multichannel location-aware interaction network for visual classificationApplied Intelligence10.1007/s10489-023-04734-x53:20(23049-23066)Online publication date: 1-Oct-2023
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Zhu QKuang WLi Z(2023)Fusing bilinear multi-channel gated vector for fine-grained classificationMachine Vision and Applications10.1007/s00138-023-01378-234:2Online publication date: 7-Feb-2023
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Zhou WXia ZDou PSu THu H(2022)Aligning Image Semantics and Label Concepts for Image Multi-Label ClassificationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/355027819:2(1-23)Online publication date: 21-Jul-2022
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