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A Discriminant Information Theoretic Learning Framework for Multi-modal Feature Representation

A Discriminant Information Theoretic Learning Framework for Multi-modal Feature Representation As sensory and computing technology advances, multi-modal features have been playing a central role in ubiquitously representing patterns and phenomena for effective information analysis and recognition. As a result, multi-modal feature representation is becoming a progressively significant direction of academic research and real applications. Nevertheless, numerous challenges remain ahead, especially in the joint utilization of discriminatory representations and complementary representations from multi-modal features. In this article, a discriminant information theoretic learning (DITL) framework is proposed to address these challenges. By employing this proposed framework, the discrimination and complementation within the given multi-modal features are exploited jointly, resulting in a high-quality feature representation. According to characteristics of the DITL framework, the newly generated feature representation is further optimized, leading to lower computational complexity and improved system performance. To demonstrate the effectiveness and generality of DITL, we conducted experiments on several recognition examples, including both static cases, such as handwritten digit recognition, face recognition, and object recognition, and dynamic cases, such as video-based human emotion recognition and action recognition. The results show that the proposed framework outperforms state-of-the-art algorithms. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Intelligent Systems and Technology (TIST) Association for Computing Machinery

A Discriminant Information Theoretic Learning Framework for Multi-modal Feature Representation

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References (161)

Publisher
Association for Computing Machinery
Copyright
Copyright © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.
ISSN
2157-6904
eISSN
2157-6912
DOI
10.1145/3587253
Publisher site
See Article on Publisher Site

Abstract

As sensory and computing technology advances, multi-modal features have been playing a central role in ubiquitously representing patterns and phenomena for effective information analysis and recognition. As a result, multi-modal feature representation is becoming a progressively significant direction of academic research and real applications. Nevertheless, numerous challenges remain ahead, especially in the joint utilization of discriminatory representations and complementary representations from multi-modal features. In this article, a discriminant information theoretic learning (DITL) framework is proposed to address these challenges. By employing this proposed framework, the discrimination and complementation within the given multi-modal features are exploited jointly, resulting in a high-quality feature representation. According to characteristics of the DITL framework, the newly generated feature representation is further optimized, leading to lower computational complexity and improved system performance. To demonstrate the effectiveness and generality of DITL, we conducted experiments on several recognition examples, including both static cases, such as handwritten digit recognition, face recognition, and object recognition, and dynamic cases, such as video-based human emotion recognition and action recognition. The results show that the proposed framework outperforms state-of-the-art algorithms.

Journal

ACM Transactions on Intelligent Systems and Technology (TIST)Association for Computing Machinery

Published: Apr 13, 2023

Keywords: Discriminative representation

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