Abstract:
To solve the problem that probabilistic Latent Semantic Analysis(pLSA) model is not suitable for large-scale image dataset, a new image classification algorithm based on Latent Dirichlet Allocation(LDA) model is proposed. It uses Bag-of-Features(BOF) model as images initial description, applies Gibbs sampling to estimate the parameters of LDA model, and gets images distribution in the latent topic space. Images are finally classified by k Nearest Neighbor(kNN) algorithm. Experimental results indicate that, compared with algorithm based on pLSA model, the image classification algorithm based on LDA has more powerful classification performances.
Key words:
Bag-of-Features(BOF) model,
middle-level semantic feature,
Latent Dirichlet Allocation(LDA) model,
latent topic distribution feature,
k Nearest Neighbor(kNN) algorithm,
image classification
摘要: 概率隐含语义分析模型不适用于大规模图像数据集,为此,提出一种基于隐含狄利克雷分配模型(LDA)的图像分类算法。以BOF特征作为图像内容的初始描述,利用Gibbs抽样算法近似估算LDA模型参数,得到图像的隐含主题分布特征,并采用k近邻算法对图像进行分类。实验结果表明,与基于概率隐含语义分析模型的分类算法相比,该算法的分类性能较优。
关键词:
BOF模型,
中层语义特征,
隐含狄利克雷分配模型,
隐含主题分布特征,
k近邻算法,
图像分类
CLC Number:
YANG Sai, DIAO Chun-Xia. Image Classification Algorithm Based on Latent Dirichlet Allocation Model[J]. Computer Engineering, 2012, 38(14): 181-183.
杨赛, 赵春霞. 基于隐含狄利克雷分配模型的图像分类算法[J]. 计算机工程, 2012, 38(14): 181-183.