☆ 4.7 Article

Label Guided Discrete Hashing for Cross-Modal Retrieval

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022)

Related references

Note: Only part of the references are listed.

Article Computer Science, Artificial Intelligence

Weakly-Supervised Enhanced Semantic-Aware Hashing for Cross-Modal Retrieval

Chao Zhang et al.

Summary: This paper proposes a weakly supervised enhanced semantic-aware hashing (WASH) method that simultaneously estimates label noises and performs enhanced semantic-aware hash learning. WASH employs low-rank and sparse decomposition to alleviate label noises and obtains high-level semantic factors and a semantic correlation matrix. The low-rank semantic factors and multi-modal features are jointly factorized into a common subspace to reduce heterogeneity gaps and enhance the semantic awareness of shared representation.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2023)

Add to Collection

Article Computer Science, Artificial Intelligence

Adaptive Label Correlation Based Asymmetric Discrete Hashing for Cross-Modal Retrieval

Huaxiong Li et al.

Summary: Hashing methods have been widely studied for cross-modal retrieval. However, most existing approaches only focus on preserving semantic similarity and ignore the label information and multi-label correlations. In this article, a new method called Adaptive Label correlation based asymmEtric Cross-modal Hashing (ALECH) is proposed, which decomposes the hash learning into two steps: hash codes learning and hash functions learning. ALECH leverages adaptive label correlations and employs an asymmetric strategy to connect latent feature space and Hamming space to preserve semantic similarity. Experimental results on benchmark datasets demonstrate that ALECH outperforms state-of-the-art cross-hashing methods.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2023)

Add to Collection

Article Engineering, Civil

Global Context Assisted Structure-Aware Vehicle Retrieval

Yan Tian et al.

Summary: In this study, a local-global context network and a structure-aware quadruple loss are proposed to address the issues of localization and utilization of negative samples in vehicle retrieval. The experimental results demonstrate that the approach achieves accuracy comparable to state-of-the-art methods in vehicle retrieval.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022)

Add to Collection

Article Automation & Control Systems

Fast Cross-Modal Hashing With Global and Local Similarity Embedding

Yongxin Wang et al.

Summary: In this paper, an efficient supervised cross-modal hashing method, Fast Cross-Modal Hashing (FCMH), is proposed. FCMH leverages both global and local similarity information in groups, addresses class correlation by partitioning training samples and embedding it into learning binary codes. Furthermore, an efficient discrete optimization algorithm with a well-designed group updating scheme is proposed, making computational complexity linear to the size of the training set, resulting in FCMH outperforming state-of-the-art approaches in terms of retrieval accuracy and learning efficiency on benchmark datasets.

IEEE TRANSACTIONS ON CYBERNETICS (2022)

Add to Collection

Article Computer Science, Information Systems

Asymmetric Correlation Quantization Hashing for Cross-Modal Retrieval

Lu Wang et al.

Summary: Cross-modal hashing (CMH) is a method that can learn and retrieve similarity across different modalities. However, existing methods have limitations in fully exploiting the underlying properties of multi-modal data and often suffer from significant quantization errors. This paper proposes a novel Asymmetric Correlation Quantization Hashing (ACQH) method to address these challenges, which learns projection matrices and constructs hash codes using semantic similarity preservation and label regression.

IEEE TRANSACTIONS ON MULTIMEDIA (2022)

Add to Collection

Article Computer Science, Artificial Intelligence

An Orthogonal Subspace Decomposition Method for Cross-Modal Retrieval

Zhixiong Zeng et al.

Summary: This article studies the importance of distinguishing and utilizing modality-shared and modality-specific information in multimodal data for cross-modal retrieval. It proposes an orthogonal subspace decomposition method and demonstrates its effectiveness through comprehensive experiments.

IEEE INTELLIGENT SYSTEMS (2022)

Add to Collection

Article Computer Science, Information Systems

Weakly Supervised Cross-Modal Hashing

Xuanwu Liu et al.

Summary: In this article, a weakly supervised cross-modal hashing method (WCHash) is proposed, which can efficiently retrieve data across different modalities. By enriching the labels of training data using a multi-label weak-label method and optimizing a latent central modality, WCHash achieves better performance in cross-modal retrieval.

IEEE TRANSACTIONS ON BIG DATA (2022)

Add to Collection

Article Computer Science, Artificial Intelligence

BATCH: A Scalable Asymmetric Discrete Cross-Modal Hashing

Yongxin Wang et al.

Summary: The paper introduces a novel supervised cross-modal hashing method that establishes a connection between the common latent space and hash codes, enabling cross-modal retrieval and embedding more similarity information into binary codes while reducing quantization error and redundancy.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

Exploiting Subspace Relation in Semantic Labels for Cross-Modal Hashing

Heng Tao Shen et al.

Summary: Cross-modal hashing method SRLCH proposed in this paper utilizes label relation information in the semantic space to enhance the similarity between different modalities. The algorithm preserves modality relationships, discrete constraints, and nonlinear structures, providing a closed-form binary codes solution.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

MTFH: A Matrix Tri-Factorization Hashing Framework for Efficient Cross-Modal Retrieval

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)

Add to Collection

Article Engineering, Civil

An Intelligent Video Analysis Method for Abnormal Event Detection in Intelligent Transportation Systems

Shaohua Wan et al.

Summary: This article proposes a long video event retrieval algorithm based on superframe segmentation, which effectively improves the efficiency and accuracy of semantic description in analyzing live video streams in intelligent transportation systems. Experimental results demonstrate its significant reduction in retrieval time.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

Asymmetric Supervised Consistent and Specific Hashing for Cross-Modal Retrieval

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)

Add to Collection

Article Engineering, Civil

Structural Analysis of Attributes for Vehicle Re-Identification and Retrieval

Yanzhu Zhao et al.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2020)

Add to Collection

Article Computer Science, Artificial Intelligence

Weakly-supervised Semantic Guided Hashing for Social Image Retrieval

Zechao Li et al.

INTERNATIONAL JOURNAL OF COMPUTER VISION (2020)

Add to Collection

Article Computer Science, Artificial Intelligence

Joint and individual matrix factorization hashing for large-scale cross-modal retrieval

Di Wang et al.

PATTERN RECOGNITION (2020)

Add to Collection

Article Computer Science, Artificial Intelligence

Model Optimization Boosting Framework for Linear Model Hash Learning

Xingbo Liu et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2020)

Add to Collection

Article Engineering, Civil

Persistent Stereo Visual Localization on Cross-Modal Invariant Map

Xiaqing Ding et al.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2020)

Add to Collection

Article Computer Science, Artificial Intelligence

Scalable Deep Hashing for Large-Scale Social Image Retrieval

Hui Cui et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2020)

Add to Collection

Article Computer Science, Artificial Intelligence

Collective Affinity Learning for Partial Cross-Modal Hashing

Jun Guo et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2020)

Add to Collection

Article Computer Science, Artificial Intelligence

Discriminative Supervised Hashing for Cross-Modal Similarity Search

Jun Yu et al.

IMAGE AND VISION COMPUTING (2019)

Add to Collection

Article Computer Science, Artificial Intelligence

Generalized Semantic Preserving Hashing for Cross-Modal Retrieval

Devraj Mandal et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2019)

Add to Collection

Article Computer Science, Information Systems

Graph-based supervised discrete image hashing

Jian Guan et al.

JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION (2019)

Add to Collection

Proceedings Paper Computer Science, Information Systems

Online Multi-modal Hashing with Dynamic Query-adaption

Xu Lu et al.

PROCEEDINGS OF THE 42ND INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '19) (2019)

Add to Collection

Article Engineering, Electrical & Electronic

Robust and Flexible Discrete Hashing for Cross-Modal Similarity Search

Di Wang et al.

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY (2018)

Add to Collection

Article Engineering, Electrical & Electronic

An Overview of Cross-Media Retrieval: Concepts, Methodologies, Benchmarks, and Challenges

Yuxin Peng et al.

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY (2018)

Add to Collection

Article Engineering, Civil

Cross-Domain Traffic Scene Understanding: A Dense Correspondence-Based Transfer Learning Approach

Shuai Di et al.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2018)

Add to Collection

Article Engineering, Civil

Supervised Hash Coding With Deep Neural Network for Environment Perception of Intelligent Vehicles

Chenggang Yan et al.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2018)

Add to Collection

Article Computer Science, Artificial Intelligence

A Survey on Learning to Hash

Jingdong Wang et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2018)

Add to Collection

Article Computer Science, Information Systems

Binary Hashing for Approximate Nearest Neighbor Search on Big Data: A Survey

Yuan Cao et al.

IEEE ACCESS (2018)

Add to Collection

Article Computer Science, Artificial Intelligence

Learning Discriminative Binary Codes for Large-scale Cross-modal Retrieval

Xing Xu et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2017)

Add to Collection

Proceedings Paper Computer Science, Artificial Intelligence

Cross-Modality Binary Code Learning via Fusion Similarity Hashing

Hong Liu et al.

30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) (2017)

Add to Collection

Article Computer Science, Artificial Intelligence

Online supervised hashing

Fatih Cakir et al.

COMPUTER VISION AND IMAGE UNDERSTANDING (2017)

Add to Collection

Article Computer Science, Artificial Intelligence

Supervised Matrix Factorization Hashing for Cross-Modal Retrieval

Jun Tang et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2016)

Add to Collection

Proceedings Paper Computer Science, Artificial Intelligence

Collective Matrix Factorization Hashing for Multimodal Data

Guiguang Ding et al.

2014 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) (2014)

Add to Collection

Article Computer Science, Artificial Intelligence

Canonical correlation analysis: An overview with application to learning methods

DR Hardoon et al.

NEURAL COMPUTATION (2004)

Add to Collection

© Peeref 2019-2024. All rights reserved.