Article

Cross-domain video concept detection using adaptive svms

Authors:

Alexander G. HauptmannAuthors Info & Claims

MM '07: Proceedings of the 15th ACM international conference on Multimedia

Pages 188 - 197

https://doi.org/10.1145/1291233.1291276

Published: 29 September 2007 Publication History

Abstract

Many multimedia applications can benefit from techniques for adapting existing classifiers to data with different distributions. One example is cross-domain video concept detection which aims to adapt concept classifiers across various video domains. In this paper, we explore two key problems for classifier adaptation: (1) how to transform existing classifier(s) into an effective classifier for a new dataset that only has a limited number of labeled examples, and (2) how to select the best existing classifier(s) for adaptation. For the first problem, we propose Adaptive Support Vector Machines (A-SVMs) as a general method to adapt one or more existing classifiers of any type to the new dataset. It aims to learn the "delta function" between the original and adapted classifier using an objective function similar to SVMs. For the second problem, we estimate the performance of each existing classifier on the sparsely-labeled new dataset by analyzing its score distribution and other meta features, and select the classifiers with the best estimated performance. The proposed method outperforms several baseline and competing methods in terms of classification accuracy and efficiency in cross-domain concept detection in the TRECVID corpus.

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Index Terms

Cross-domain video concept detection using adaptive svms
1. Information systems
  1. Information retrieval
    1. Document representation

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Published In

cover image ACM Conferences

MM '07: Proceedings of the 15th ACM international conference on Multimedia

September 2007

1115 pages

ISBN:9781595937025

DOI:10.1145/1291233

General Chairs:
Rainer Lienhart
University of Augsburg, Germany
,
Anand R. Prasad
DoCoMo Euro-Labs,Germany
,
Program Chairs:
Alan Hanjalic
Delft University of Technology, The Netherlands
,
Sunghyun Choi
Seoul National University, South Korea
,
Brian Bailey
University of Illinois at Urbana-Champaign
,
Nicu Sebe
University of Amsterdam, The Netherlands

Copyright © 2007 ACM.

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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Published: 29 September 2007

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MM07

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MM07: The 15th ACM International Conference on Multimedia 2007

September 25 - 29, 2007

Augsburg, Germany

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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https://doi.org/10.1016/j.nucengdes.2025.113956
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https://doi.org/10.1007/s11571-024-10216-8
Hossen MAwrangjeb MPan SMamun A(2025)Transfer learning in agriculture: a reviewArtificial Intelligence Review10.1007/s10462-024-11081-x58:4Online publication date: 25-Jan-2025
https://doi.org/10.1007/s10462-024-11081-x
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