.png){kind=icon}
返回
Nonnegative Multiresolution Representation-Based Texture Image Classification
.png){kind=icon}
PRE
AI
DOI：10.1145/2738050.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Effective representation of image texture is important for an image-classification task. Statistical modelling in wavelet domains has been widely used to image texture representation. However, due to the intraclass complexity and interclass diversity of textures, it is hard to use a predefined probability distribution function to fit adaptively all wavelet subband coefficients of different textures. In this article, we propose a novel modelling approach, Heterogeneous and Incrementally Generated Histogram (HIGH), to indirectly model the wavelet coefficients by use of four local features in wavelet subbands. By concatenating all the HIGHs in all wavelet subbands of a texture, we can construct a nonnegative multiresolution vector (NMV) to represent a texture image. Considering the NMV's high dimensionality and nonnegativity, we further propose a Hessian regularized discriminative nonnegative matrix factorization to compute a low-dimensional basis of the linear subspace of NMVs. Finally, we present a texture classification approach by projecting NMVs on the low-dimensional basis. Experimental results show that our proposed texture classification method outperforms seven representative approaches.
Keyword:
Theory
Experimentation
Nonnegative matrix factorization
texture classification
histogram
manifold regularization
Hessian regularization
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.6
论文数:
1.5K
被引数:
6.2K
机构
引用论文
.png){kind=icon}
From Heuristic Optimization to Dictionary Learning: A Review and Comprehensive Comparison of Image Denoising Algorithms从启发式优化到字典学习: 图像去噪算法的回顾与综合比较
PREAI
分享收藏Nanocrystalline silicon films as multifunctional material for optoelectronic and photovoltaic applications纳米硅薄膜作为光电和光伏应用的多功能材料
PREAI
分享收藏
查看更多内容