摘要
提出了一种基于分块潜在语义的场景分类方法。该方法首先对图像进行均匀分块并使用分块内视觉词汇的出现频率来描述每一个分块,然后利用概率潜在语义分析(PLSA)方法从图像的分块集合中发现潜在语义模型,最后利用该模型提取出潜在语义在图像分块中的出现情况来进行场景分类。在13类场景图像上的实验表明,与其他方法相比,该方法具有更高的分类准确率。
A novel scene classification method was presented based on block latent semantic. The image blocks were first extracted on a regular grid and the visual words in blocks were used to describe every block, and then block latent semantic models were achieved by using Probabilistic Latent Semantic Analysis （ PLSA）. The latent semantic model was used to find the latent semantic in image block and their spatial distribute in image. Finally, this feature was used to construct a SVM model to classify scene. Experimental results show that this method has satisfactory classification performances on a large set of 13 categories of complex scenes.
出处
《计算机应用》
CSCD
北大核心
2008年第6期1537-1539,1542,共4页
journal of Computer Applications
基金
国家自然科学基金资助项目(60473117)
国家863计划项目(2006AA01Z319)
关键词
场景分类
分块潜在语义
视觉词汇
局部不变特征
概率潜在语义分析
scene classification
block latent semantic
visual word
local invariant feature
Probabilistic Latent Semantic Analysis （PLSA）
