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Article
Computer Science, Artificial Intelligence
Bedirhan Uzun et al.
Summary: This article presents two methods for finding compact deep feature models that approximate images in set-based face recognition problems. The first method treats each image set as a nonlinear face manifold composed of linear components, approximating each subset using the center of the deep feature representations. The second method uses discriminative common vectors to represent image features in subsets, approximating the entire subset with an affine hull. Experimental results show that both methods achieve state-of-the-art accuracies on tested image datasets.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Engineering, Electrical & Electronic
Yuan Cao et al.
Summary: This paper analyzes the accuracy loss issue in the quantization step of hashing algorithms and proposes two new quantization methods: Variable Integer-based Quantization (VIQ) and Variable Codebook-based Quantization (VCQ). Experimental results show that both methods can improve accuracy, with VCQ performing better than VIQ, but VIQ providing higher search efficiency.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Yong Chen et al.
Summary: Due to the growth of multimedia data, multi-modal hashing is attracting attention as a scalable technique for cross-view retrieval. Existing methods often have issues and ignore important discrete constraints. This paper proposes Enhanced Discrete Multi-modal Hashing (EDMH) which addresses these limitations and shows better performance and speed in experiments.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Rui Wang et al.
Summary: A SymNet network was proposed for image set classification, which utilized SPD matrix mapping layers, rectifying layers, pooling layers, and log-map layer to achieve effective feature learning and data compression. PCA and KDA algorithms were applied for discriminative subspace learning, and extensive experiments validated the feasibility and effectiveness of the proposed SymNet.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Information Systems
Rui Wang et al.
Summary: This study discusses the importance of recognizing wild video based image sets and proposes a novel algorithm to model image sets from a multi-geometric perspective for improved classification performance.
IEEE TRANSACTIONS ON BIG DATA
(2022)
Article
Computer Science, Artificial Intelligence
Xin Liu et al.
Summary: The study introduces a fast discriminative discrete hashing method, using orthogonal rotation of semantic data and introducing orthogonal basis and epsilon-dragging technique to maximize the discriminative power of semantic information. Additionally, an orthogonal transformation scheme is proposed to ensure consistency between data features and semantic representation, improving retrieval performance.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Dong Wei et al.
Summary: In this paper, a novel Discrete Metric Learning (DML) approach based on the Riemannian manifold is proposed for fast image set classification. It achieves competitive performance and efficiency compared to existing methods.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2022)
Article
Computer Science, Artificial Intelligence
Xin Liu et al.
Summary: This paper proposes a Matrix Tri-Factorization Hashing (MTFH) framework, which is generalized and flexible in cross-modal retrieval. It can adapt to various scenarios of multi-modal data and learn hash codes of different lengths to better handle challenging tasks.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2021)
Article
Computer Science, Information Systems
Guoqing Zhang et al.
Summary: The paper proposes an optimal discriminative feature and dictionary learning method for image set classification, aiming to learn powerful representations from multiple images. The method enhances inter-class separation and reduces intra-class scatter through learning feature mapping matrix and dictionary.
INFORMATION SCIENCES
(2021)
Article
Automation & Control Systems
Zhenwen Ren et al.
Summary: In this article, we propose a novel pure graph-based MKC method which preserves the local manifold structure of the data in kernel space by learning multiple candidate graphs. The method fully considers the latent consistency and selfishness of these candidate graphs, and introduces a graph connectivity constraint to avoid requiring any postprocessing clustering step, demonstrating its superiority through comprehensive experimental results.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Computer Science, Information Systems
Zheng Zhang et al.
Summary: This article introduces a novel Probability Ordinal-preserving Semantic Hashing (POSH) framework, which derives the whole learning framework of the ordinal similarity-preserving hashing based on maximum posteriori estimation, jointly considering probabilistic ordinal similarity preservation, probabilistic quantization function, and probabilistic semantic-preserving function. Extensive experiments validate the superiority of the proposed method against state-of-the-art hashing-based retrieval methods.
ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
(2021)
Article
Computer Science, Artificial Intelligence
Zhenwen Ren et al.
Summary: The study introduces a new multiple kernel learning method (SPMKC) that preserves the global and local structure of input data in kernel space through a new kernel weight strategy and a kernel adaptive local structure learning term.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Xingbo Liu et al.
Summary: This study proposes a novel reinforced short-length hashing method that balances accuracy and training expenditure through mutual reconstruction and pairwise similarity matrix. Experimental results on three large-scale image benchmarks demonstrate the superior performance of RSLH in various short-length hashing scenarios.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2021)
Article
Automation & Control Systems
Zhenwen Ren et al.
Summary: This article proposes a new MKGC method that directly learns a consensus affinity graph from multiple candidate affinity graphs, synthesized through a thin autoweighted fusion model. Experimental results demonstrate that the proposed method consistently outperforms state-of-the-art methods in accuracy and performance.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Yongxin Wang et al.
Summary: This study introduces an efficient sparse hashing method for cross-modal retrieval tasks, achieving superior performance compared to state-of-the-art approaches. By properly utilizing sparse coding and discrete optimization algorithms, the method reduces quantization errors and improves the discriminative power of hash codes. Experimental results demonstrate the efficiency and effectiveness of the proposed high-dimensional sparse cross-modal hashing approach.
PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2021 (WWW 2021)
(2021)
Article
Computer Science, Information Systems
Rui Wang et al.
Summary: This paper introduces a graph embedding multi-kernel metric learning (GEMKML) algorithm for image set classification, addressing the challenges of establishing appropriate image set models and measuring similarity between image sets. The proposed algorithm implements set modeling, feature extraction, and classification, by constructing a novel cascaded feature learning architecture and a graph embedding multi-kernel metric learning scheme.
IEEE TRANSACTIONS ON MULTIMEDIA
(2021)
Article
Computer Science, Artificial Intelligence
Min Meng et al.
Summary: This article introduces a novel supervised cross-modal hashing method ASCSH, which decomposes mapping matrices to exploit correlation between modalities and uses a discrete asymmetric framework to fully explore supervised information, solving binary constraint problems effectively.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2021)
Article
Computer Science, Artificial Intelligence
Wenjie Zhu et al.
APPLIED INTELLIGENCE
(2020)
Article
Computer Science, Information Systems
Yuebin Wang et al.
IEEE TRANSACTIONS ON MULTIMEDIA
(2020)
Article
Computer Science, Artificial Intelligence
Xiushan Nie et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2020)
Article
Automation & Control Systems
Shiyuan He et al.
IEEE TRANSACTIONS ON CYBERNETICS
(2020)
Article
Computer Science, Artificial Intelligence
Chaoqun Zheng et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2020)
Article
Computer Science, Artificial Intelligence
Zhenwen Ren et al.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2020)
Article
Computer Science, Artificial Intelligence
Mingbao Lin et al.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2020)
Article
Computer Science, Artificial Intelligence
Lei Zhu et al.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2020)
Article
Computer Science, Artificial Intelligence
Yong Chen et al.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2020)
Article
Computer Science, Artificial Intelligence
Wenzhu Yan et al.
Article
Computer Science, Artificial Intelligence
Jie Gui et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2018)
Article
Computer Science, Artificial Intelligence
Jingdong Wang et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2018)
Proceedings Paper
Computer Science, Information Systems
Xin Luo et al.
ACM/SIGIR PROCEEDINGS 2018
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Jie Feng et al.
2017 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2017)
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Liang Chen
2014 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
(2014)
Article
Computer Science, Artificial Intelligence
Yiqun Hu et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2012)
