research-article

Boosted Multifeature Learning for Cross-Domain Transfer

Authors:

Ming-Hsuan YangAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 11, Issue 3

Article No.: 35, Pages 1 - 18

https://doi.org/10.1145/2700286

Published: 05 February 2015 Publication History

Abstract

Conventional learning algorithm assumes that the training data and test data share a common distribution. However, this assumption will greatly hinder the practical application of the learned model for cross-domain data analysis in multimedia. To deal with this issue, transfer learning based technology should be adopted. As a typical version of transfer learning, domain adaption has been extensively studied recently due to its theoretical value and practical interest. In this article, we propose a boosted multifeature learning (BMFL) approach to iteratively learn multiple representations within a boosting procedure for unsupervised domain adaption. The proposed BMFL method has a number of properties. (1) It reuses all instances with different weights assigned by the previous boosting iteration and avoids discarding labeled instances as in conventional methods. (2) It models the instance weight distribution effectively by considering the classification error and the domain similarity, which facilitates learning new feature representation to correct the previously misclassified instances. (3) It learns multiple different feature representations to effectively bridge the source and target domains. We evaluate the BMFL by comparing its performance on three applications: image classification, sentiment classification and spam filtering. Extensive experimental results demonstrate that the proposed BMFL algorithm performs favorably against state-of-the-art domain adaption methods.

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Cited By

Jiao YCai CBao B(2025)Unified Text-Image Space Alignment with Cross-Modal Prompting in CLIP for UDAACM Transactions on Multimedia Computing, Communications, and Applications10.1145/3715699Online publication date: 5-Feb-2025
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Huang YYang XZhang JXu CMagalhães Jdel Bimbo ASatoh SSebe NAlameda-Pineda XJin QOria VToni L(2022)Relative Alignment Network for Source-Free Multimodal Video Domain AdaptationProceedings of the 30th ACM International Conference on Multimedia10.1145/3503161.3548009(1652-1660)Online publication date: 10-Oct-2022
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Index Terms

Boosted Multifeature Learning for Cross-Domain Transfer
1. Applied computing
  1. Document management and text processing
2. Information systems
  1. World Wide Web
    1. Web interfaces
      1. Browsers

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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 11, Issue 3

January 2015

173 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/2733235

Editor:
Ralf Steinmetz
Technische Universität Darmstadt, Germany

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Copyright © 2015 ACM.

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Publication History

Published: 05 February 2015

Accepted: 01 September 2014

Revised: 01 August 2014

Received: 01 March 2014

Published in TOMM Volume 11, Issue 3

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Funding Sources

National Program on Key Basic Research Project (973 Program, Project No. 2012CB316304)
Singapore National Research Foundation under its International Research Centre@Singapore Funding Initiative
National Natural Science Foundation of China
National Natural Science Foundation of China (61225009, 61303173)
IDM Programme Office

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Cited By

Jiao YCai CBao B(2025)Unified Text-Image Space Alignment with Cross-Modal Prompting in CLIP for UDAACM Transactions on Multimedia Computing, Communications, and Applications10.1145/3715699Online publication date: 5-Feb-2025
https://dl.acm.org/doi/10.1145/3715699
Yamni MDaoui AKarmouni HSayyouri MQjidaa HWang CJamil M(2023)A Powerful Zero-Watermarking Algorithm for Copyright Protection of Color Images Based on Quaternion Radial Fractional Hahn Moments and Artificial Bee Colony AlgorithmCircuits, Systems, and Signal Processing10.1007/s00034-023-02379-242:9(5602-5633)Online publication date: 26-Apr-2023
https://dl.acm.org/doi/10.1007/s00034-023-02379-2
Huang YYang XZhang JXu CMagalhães Jdel Bimbo ASatoh SSebe NAlameda-Pineda XJin QOria VToni L(2022)Relative Alignment Network for Source-Free Multimodal Video Domain AdaptationProceedings of the 30th ACM International Conference on Multimedia10.1145/3503161.3548009(1652-1660)Online publication date: 10-Oct-2022
https://doi.org/10.1145/3503161.3548009
Li JLiu WZhou YYu JTao DXu C(2022)Domain-invariant Graph for Adaptive Semi-supervised Domain AdaptationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/348719418:3(1-18)Online publication date: 4-Mar-2022
https://dl.acm.org/doi/10.1145/3487194
Hosny KDarwish M(2022)Robust color image watermarking using multiple fractional-order moments and chaotic mapMultimedia Tools and Applications10.1007/s11042-022-12282-881:17(24347-24375)Online publication date: 1-Jul-2022
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Hosny KMagdi ALashin NEl-Komy OSalah A(2022)Robust color image watermarking using multi-core Raspberry pi clusterMultimedia Tools and Applications10.1007/s11042-022-12037-581:12(17185-17204)Online publication date: 1-May-2022
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Yamni MKarmouni HSayyouri MQjidaa H(2022)Quaternion cartesian fractional hahn moments for color image analysisMultimedia Tools and Applications10.1007/s11042-021-11432-881:1(737-758)Online publication date: 1-Jan-2022
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Shi SLuo THuang JDu M(2021)A Novel HDR Image Zero-Watermarking Based on Shift-Invariant Shearlet TransformSecurity and Communication Networks10.1155/2021/66627002021Online publication date: 1-Jan-2021
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Hosny KDarwish M(2021)Reversible Color Image Watermarking Using Fractional-Order Polar Harmonic Transforms and a Chaotic Sine MapCircuits, Systems, and Signal Processing10.1007/s00034-021-01756-z40:12(6121-6145)Online publication date: 1-Dec-2021
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Zhao LChen ZYang LDeen MWang Z(2019)Deep Semantic Mapping for Heterogeneous Multimedia Transfer Learning Using Co-Occurrence DataACM Transactions on Multimedia Computing, Communications, and Applications10.1145/324105515:1s(1-21)Online publication date: 24-Jan-2019
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