Join multiple Riemannian manifold representation and multi-kernel non-redundancy for image clustering

IF 8.4 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Mengyuan Zhang, Jinglei Liu
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引用次数: 0

Abstract

Image clustering has received significant attention due to the growing importance of image recognition. Researchers have explored Riemannian manifold clustering, which is capable of capturing the non-linear shapes found in real-world datasets. However, the complexity of image data poses substantial challenges for modelling and feature extraction. Traditional methods such as covariance matrices and linear subspace have shown promise in image modelling, and they are still in their early stages and suffer from certain limitations. However, these include the uncertainty of representing data using only one Riemannian manifold, limited feature extraction capacity of single kernel functions, and resulting incomplete data representation and redundancy. To overcome these limitations, the authors propose a novel approach called join multiple Riemannian manifold representation and multi-kernel non-redundancy for image clustering (MRMNR-MKC). It combines covariance matrices with linear subspace to represent data and applies multiple kernel functions to map the non-linear structural data into a reproducing kernel Hilbert space, enabling linear model analysis for image clustering. Additionally, the authors use matrix-induced regularisation to improve the clustering kernel selection process by reducing redundancy and assigning lower weights to identical kernels. Finally, the authors also conducted numerous experiments to evaluate the performance of our approach, confirming its superiority to state-of-the-art methods on three benchmark datasets.

Abstract Image

加入多黎曼流形表示和多核非冗余性以进行图像聚类
由于图像识别的重要性与日俱增,图像聚类受到了广泛关注。研究人员探索了黎曼流形聚类,它能够捕捉现实世界数据集中的非线性形状。然而,图像数据的复杂性给建模和特征提取带来了巨大挑战。协方差矩阵和线性子空间等传统方法在图像建模方面已显示出良好的前景,但这些方法仍处于早期阶段,存在一定的局限性。然而,这些局限包括仅使用一个黎曼流形表示数据的不确定性、单核函数的特征提取能力有限,以及由此导致的数据表示不完整和冗余。为了克服这些局限性,作者提出了一种名为 "联合多黎曼流形表示和多核非冗余图像聚类(MRMNR-MKC)"的新方法。它将协方差矩阵与线性子空间相结合来表示数据,并应用多核函数将非线性结构数据映射到重现核希尔伯特空间,从而实现图像聚类的线性模型分析。此外,作者还利用矩阵诱导正则化技术,通过减少冗余和为相同内核分配较低权重来改进聚类内核选择过程。最后,作者还进行了大量实验来评估我们的方法的性能,并在三个基准数据集上证实其优于最先进的方法。
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来源期刊
CAAI Transactions on Intelligence Technology
CAAI Transactions on Intelligence Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
11.00
自引率
3.90%
发文量
134
审稿时长
35 weeks
期刊介绍: CAAI Transactions on Intelligence Technology is a leading venue for original research on the theoretical and experimental aspects of artificial intelligence technology. We are a fully open access journal co-published by the Institution of Engineering and Technology (IET) and the Chinese Association for Artificial Intelligence (CAAI) providing research which is openly accessible to read and share worldwide.
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