计算机工程2012，Vol.38Issue(14)：181-183,3.DOI:10.3969/j.issn.1000-3428.2012.14.054
基于隐含狄利克雷分配模型的图像分类算法
Image Classification Algorithm Based on Latent Dirichlet Allocation Model
摘要
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(LOA) 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.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分类
信息技术与安全科学引用本文复制引用
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):181-183,3.基金项目
国家自然科学基金资助重大项目(90820306) （90820306）
