Unsupervised Visual Dictionary Construction Methods Via Clustering Analysis

XU Wang-ming; ZHENG Chao-bing

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Unsupervised Visual Dictionary Construction Methods Via Clustering Analysis

更新时间：2026-09-04

- Unsupervised Visual Dictionary Construction Methods Via Clustering Analysis
- Vol. 33, Issue 3, Pages: 155-160(2016)
- 作者机构：
  
  武汉科技大学机构信息科学与工程学院,湖北,武汉,430081
- 作者简介：
- 基金信息：
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- Published：2016
- 稿件说明：
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徐望明, 郑超兵. 基于聚类分析的无监督视觉词典构造方法研究[J]. 微电子学与计算机, 2016, 33(3): 155-160.

XU Wang-ming, ZHENG Chao-bing. Unsupervised Visual Dictionary Construction Methods Via Clustering Analysis[J]. 2016, 33(3): 155-160.
徐望明, 郑超兵. 基于聚类分析的无监督视觉词典构造方法研究[J]. 微电子学与计算机, 2016, 33(3): 155-160. DOI：

XU Wang-ming, ZHENG Chao-bing. Unsupervised Visual Dictionary Construction Methods Via Clustering Analysis[J]. 2016, 33(3): 155-160. DOI：

摘要

视觉词典是运用视觉词袋模型有效表示图像的基础和关键.研究了基于聚类分析的无监督视觉词典构造方法

针对K-Means和层次K-Means(HKM)聚类算法在构造视觉词典时对初始值敏感和容易陷入局部最优的问题进行改进

提出了一种基于谱聚类的视觉词典构造方法

采用对观测数据的相似性矩阵进行特征值分解的方法来实现聚类

可在任意分布的特征空间收敛到全局最优.通过图像检索实验评估了随机采样、K-Means、HKM和改进的谱聚类视觉词典的性能

验证了聚类分析尤其是改进的谱聚类方法构造视觉词典的有效性.

Abstract

Visual dictionary is the basic and key toefficiently represent image with Bag of Visual Words (BoVW) Model. The unsupervised visual dictionary construction methods based on clustering analysis are researched in this paper. Aiming at the problems that K-Means based clustering methods (K-Means and HKM) are sensitive to the initialization and easy to converge to the local optimum

a modified spectral clustering method is proposed to construct visual dictionary

which works in a way to cluster observations via eigenvalue decomposition according to their similarity matrix and can converge to the global optimal regardless of the distribution of feature space. The performance of visual dictionaries constructed by Random Sampling

K-Means

HKM and the improved Spectral Clustering is assessed through image retrieval experiments

and the effectiveness of clustering analysis especially the improved Spectral Clustering method to construct visual dictionary is validated.

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references

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