short-paper

Shared feature extraction for semi-supervised image classification

Authors:

Stephen MaybankAuthors Info & Claims

MM '11: Proceedings of the 19th ACM international conference on Multimedia

Pages 1165 - 1168

https://doi.org/10.1145/2072298.2071965

Published: 28 November 2011 Publication History

Abstract

Multi-task learning (MTL) plays an important role in image analysis applications, e.g. image classification, face recognition and image annotation. That is because MTL can estimate the latent shared subspace to represent the common features given a set of images from different tasks. However, the geometry of the data probability distribution is always supported on an intrinsic image sub-manifold that is embedded in a high dimensional Euclidean space. Therefore, it is improper to directly apply MTL to multiclass image classification. In this paper, we propose a manifold regularized MTL (MRMTL) algorithm to discover the latent shared subspace by treating the high-dimensional image space as a sub-manifold embedded in an ambient space. We conduct experiments on the PASCAL VOC'07 dataset with 20 classes and the MIR dataset with 38 classes by comparing MRMTL with conventional MTL and several representative image classification algorithms. The results suggest that MRMTL can properly extract the common features for image representation and thus improve the generalization performance of the image classification models.

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Cited By

Xiaozhao Fang Yong Xu Xuelong Li Zhihui Lai Wong W(2015)Learning a Nonnegative Sparse Graph for Linear RegressionIEEE Transactions on Image Processing10.1109/TIP.2015.242554524:9(2760-2771)Online publication date: Sep-2015
https://doi.org/10.1109/TIP.2015.2425545
Wang ZFeng J(2013)Multi-class learning from class proportionsNeurocomputing10.1016/j.neucom.2013.03.031119(273-280)Online publication date: Nov-2013
https://doi.org/10.1016/j.neucom.2013.03.031

Index Terms

Shared feature extraction for semi-supervised image classification
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision representations
  2. Machine learning

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Published In

cover image ACM Conferences

MM '11: Proceedings of the 19th ACM international conference on Multimedia

November 2011

944 pages

ISBN:9781450306164

DOI:10.1145/2072298

General Chairs:
K. Selçuk Candan
Arizona State University, USA
,
Sethuraman Panchanathan
Arizona State University, USA
,
Balakrishnan Prabhakaran
University of Texas at Dallas, USA
,
Program Chairs:
Hari Sundaram
Arizona State University, USA
,
Wu-Chi Feng
Portland State University, USA
,
Nicu Sebe
University of Trento, Italy

Copyright © 2011 ACM.

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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 November 2011

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SIGMM

MM '11: ACM Multimedia Conference

November 28 - December 1, 2011

Arizona, Scottsdale, USA

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Cited By

Xiaozhao Fang Yong Xu Xuelong Li Zhihui Lai Wong W(2015)Learning a Nonnegative Sparse Graph for Linear RegressionIEEE Transactions on Image Processing10.1109/TIP.2015.242554524:9(2760-2771)Online publication date: Sep-2015
https://doi.org/10.1109/TIP.2015.2425545
Wang ZFeng J(2013)Multi-class learning from class proportionsNeurocomputing10.1016/j.neucom.2013.03.031119(273-280)Online publication date: Nov-2013
https://doi.org/10.1016/j.neucom.2013.03.031

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