目的 传统潜在语义分析（LSA）方法无法获得场景目标空间分布信息和潜在主题的判别信息。方法 针对这一问题提出了一种基于多尺度空间判别性概率潜在语义分析（PLSA）的场景分类方法。首先通过空间金字塔方法对图像进行空间多尺度划分获得图像空间信息，结合PLSA模型获得每个局部块的潜在语义信息；然后串接每个特定局部块中的语义信息得到图像多尺度空间潜在语义信息；最后结合提出的权值学习方法来学习不同图像主题间的判别信息，从而得到图像的多尺度空间判别性潜在语义信息，并将学习到的权值信息嵌入支持向量机（Support Vector Machine， SVM）分类器中完成图像的场景分类。结果 在常用的3个场景图像库（Scene-13、Scene-15和Caltech-101）上的实验结果表明，本文方法平均分类精度比现有许多state-of-art方法均优。结论 充分说明了空间信息和判别性信息在场景分类中的重要性，并进一步验证了其有效性和鲁棒性。

Objective Due to the problem that traditional latent semantic analysis (LSA) method is unable to obtain spatial distribution information of objects and discriminative information of latent topic. Method We propose a scene classification approach based on multi-scale spatial discriminative probabilistic latent semantic analysis (PLSA). First, it decomposes images in multiple scales using a spatial pyramid approach to obtain spatial distribution information for images. Then, the PLSA model is used to extract the latent semantic information of each local block. Next, the latent semantic features of all local blocks are concatenated with different weights to produce the multi-scale spatial latent semantic information of image. Finally, we exploit weight learning method to learn the discriminative information between different image topics and get multi-scale spatial discriminative latent semantic information of image. Afterwards, the weight information is integrated into the support vector machine (SVM) classifier to perform image classification. Result Experimental results on the common three scene image datasets, viz. Scene-13, Scene-15 and Caltech-101, demonstrate that our method performs much better than the existing state-of-the-art approaches. Conclusion Which demonstrate the importance of spatial information and discriminative information in image classification and further verify the effectiveness and robustness of our approach.