MTRF:融合空间信息的主题模型

针对主题模型中词汇独立性和主题独立性假设忽略了视觉词汇间空间关系的问题,提出了一种融合了视觉词汇空间信息的主题模型,称为马尔可夫主题随机场(MTRF),并且提出了主题在图像处理中的表现形式为对象的组成部件。根据相邻视觉词汇以很大概率产生于同一主题的特点,该算法在产生主题的过程中,通过视觉词汇间是否产生于同一主题,来判断主题产生于马尔可夫随机场(MRF),还是产生于多项式分布。同时,从理论和实验两方面论证了主题并非对象的实例,而是以中层特征的形式表达对象的各个组成部件。与隐狄利克雷分配(LDA)相比,MTRF在Caltech101上的平均准确率提高了3.91%;在VOC2007数据集上的平均精度均值(mAP)提高了2.03%;此外,MTRF更准确地为视觉词汇分配了主题,能产生更有效表达对象的组成部件的中层特征。实验结果表明,MTRF有效地利用了空间信息,提高了模型的准确率。

MTRF: a topic model with spatial information

To overcome the limitation of the assumptions of topic model-word independence and topic independence, a topic model which inosculated the spatial relationship of visual words was proposed, namely Markov Topic Random Field (MTRF). In addition, it was discussed that the "topic" of topic model represented the part of object in image processing. There is a high probability of the neighbor visual words generated from the same topic, and whether the visual words were generated from the same topic determined the topic was generated from Markov Random Field (MRF) or multinomial distribution of topic model. Meanwhile, both theoretical analysis and experimental results prove that "topic" of topic model appeared as mid-level feature to represent the parts of objects rather than the instances of objects. In experiments of image classification, the average accuracy of MTRF was 3.91% higher than that of Latent Dirichlet Allocation (LDA) on Caltech101 dataset, and the mean Average Precision (mAP) of MTRF was 2.03% higher than that of LDA on VOC2007 dataset. Furthermore, MTRF assigned topics to visual words more accurately and got the mid-level features which represented the parts of objects more effectively than LDA. The experimental results show that MTRF makes use of the spatial information effectively and improves the accuracy of the model.