中文摘要：

图像目标检测的任务是通过对图像分块或者分区域提取特征，进行学习和分类，从而检测出目标在图像中的位置．基于潜在迪利克雷分布模型，提出一种应用于目标检测的主题模型latent Dirichlet classification（LDC），结合图像连续值局部特征和共生关系来进行目标检测．LDC模型将latent Dirichlet allocation（LDA）生成的主题信息作为权重赋予样本，生成多份样本，然后利用多份样本训练多个分类器进行集成分类．实验结果表明利用LDC模型能有效提高检测精度．

英文摘要：

Object detection and recognition is a hot topic in computer vision. Traditional methods use only local features for detection. Recently, some research results show that the detecting performance could be improving by using topic features. Some researchers employed topic models which is originally used for text analysis to extract topic features from images for object recognition and detection. However, visual features should be quantized into virtual words and information of class label should be ignored while using traditional topic models such as probabilistic latent sematic analysis （PLSA）, latent Dirichlet allocation （LDA） and so on. In order to utilize continuous local features and information of class label in one model, we propose a new graphical model named latent Dirichlet classification（LDC）, which is inspired by LDA model. The proposed model has three more variables than LDA in the graphical structure： x（loeal features）, c（class label） and v（parameter）. In the proposed model, we consider class label of each image block is determined by both of its local features and topic features based on original LDA model. Parameter v is a set of elassifiers trained for combining these two features. Similar with the inference process of LDA model, we use variational inference to solve our model. As a result of continuous local features, information of class label and topic features are all token into consideration reasonably, LDC ean be used in object detection directly and efficiently. In the end of this paper, we test the availability of LDC on two datasets. Experimental results show that our proposed model improve the performance of object detection efficiently.