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research-article

Discriminative Learning and Recognition of Image Set Classes Using Canonical Correlations

Authors:

Roberto CipollaAuthors Info & Claims

IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 29, Issue 6

Pages 1005 - 1018

https://doi.org/10.1109/TPAMI.2007.1037

Published: 01 June 2007 Publication History

Abstract

We address the problem of comparing sets of images for object recognition, where the sets may represent variations in an object's appearance due to changing camera pose and lighting conditions. Canonical Correlations (also known as principal or canonical angles), which can be thought of as the angles between two d\hbox{-}\rm dimensional subspaces, have recently attracted attention for image set matching. Canonical correlations offer many benefits in accuracy, efficiency, and robustness compared to the two main classical methods: parametric distribution-based and nonparametric sample-based matching of sets. Here, this is first demonstrated experimentally for reasonably sized data sets using existing methods exploiting canonical correlations. Motivated by their proven effectiveness, a novel discriminative learning method over sets is proposed for set classification. Specifically, inspired by classical Linear Discriminant Analysis (LDA), we develop a linear discriminant function that maximizes the canonical correlations of within-class sets and minimizes the canonical correlations of between-class sets. Image sets transformed by the discriminant function are then compared by the canonical correlations. Classical orthogonal subspace method (OSM) is also investigated for the similar purpose and compared with the proposed method. The proposed method is evaluated on various object recognition problems using face image sets with arbitrary motion captured under different illuminations and image sets of 500 general objects taken at different views. The method is also applied to object category recognition using ETH-80 database. The proposed method is shown to outperform the state-of-the-art methods in terms of accuracy and efficiency.

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Cited By

Shen XWu WWang XZheng Y(2024)Multiple Riemannian Kernel Hashing for Large-Scale Image Set Classification and RetrievalIEEE Transactions on Image Processing10.1109/TIP.2024.341941433(4261-4273)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TIP.2024.3419414
Hachiya HSaito Y(2024)Set representative vector and its asymmetric attention-based transformation for heterogeneous set-to-set matchingNeurocomputing10.1016/j.neucom.2024.127372578:COnline publication date: 14-Apr-2024
https://dl.acm.org/doi/10.1016/j.neucom.2024.127372
Ma JTang YShang Z(2024)Discriminative latent subspace learning with adaptive metric learningNeural Computing and Applications10.1007/s00521-023-09159-836:4(2049-2066)Online publication date: 1-Feb-2024
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Index Terms

Discriminative Learning and Recognition of Image Set Classes Using Canonical Correlations
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object recognition
  2. Machine learning
    1. Machine learning approaches

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Published In

cover image IEEE Transactions on Pattern Analysis and Machine Intelligence

IEEE Transactions on Pattern Analysis and Machine Intelligence Volume 29, Issue 6

June 2007

190 pages

ISSN:0162-8828

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Copyright © 2007.

Publisher

IEEE Computer Society

United States

Publication History

Published: 01 June 2007

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Cited By

Shen XWu WWang XZheng Y(2024)Multiple Riemannian Kernel Hashing for Large-Scale Image Set Classification and RetrievalIEEE Transactions on Image Processing10.1109/TIP.2024.341941433(4261-4273)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TIP.2024.3419414
Hachiya HSaito Y(2024)Set representative vector and its asymmetric attention-based transformation for heterogeneous set-to-set matchingNeurocomputing10.1016/j.neucom.2024.127372578:COnline publication date: 14-Apr-2024
https://dl.acm.org/doi/10.1016/j.neucom.2024.127372
Ma JTang YShang Z(2024)Discriminative latent subspace learning with adaptive metric learningNeural Computing and Applications10.1007/s00521-023-09159-836:4(2049-2066)Online publication date: 1-Feb-2024
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