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Latent Dirichlet Allocation Models for Image Classification

机译:潜在狄利克雷分配模型用于图像分类

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Two new extensions of latent Dirichlet allocation (LDA), denoted topic-supervised LDA (ts-LDA) and class-specific-simplex LDA (css-LDA), are proposed for image classification. An analysis of the supervised LDA models currently used for this task shows that the impact of class information on the topics discovered by these models is very weak in general. This implies that the discovered topics are driven by general image regularities, rather than the semantic regularities of interest for classification. To address this, ts--LDA models are introduced which replace the automated topic discovery of LDA with specified topics, identical to the classes of interest for classification. While this results in improvements in classification accuracy over existing LDA models, it compromises the ability of LDA to discover unanticipated structure of interest. This limitation is addressed by the introduction of css-LDA, an LDA model with class supervision at the level of image features. In css-LDA topics are discovered per class, i.e., a single set of topics shared across classes is replaced by multiple class-specific topic sets. The css-LDA model is shown to combine the labeling strength of topic-supervision with the flexibility of topic-discovery. Its effectiveness is demonstrated through an extensive experimental evaluation, involving multiple benchmark datasets, where it is shown to outperform existing LDA-based image classification approaches.
机译:提出了两个新的潜在狄利克雷分配(LDA)扩展,分别表示为主题监督LDA(ts-LDA)和特定于类的简单LDA(css-LDA),用于图像分类。对当前用于此任务的监督LDA模型的分析表明,班级信息对这些模型发现的主题的影响通常非常微弱。这意味着发现的主题是由一般的图像规律性驱动的,而不是由分类感兴趣的语义规律性驱动的。为了解决这个问题,引入了ts-LDA模型,该模型用指定的主题替换了LDA的自动主题发现,该主题与分类的兴趣类别相同。尽管与现有的LDA模型相比,分类精度得到了提高,但它损害了LDA发现意外结构的能力。通过引入css-LDA(在图像特征级别具有类监督的LDA模型)可以解决此限制。在css-LDA中,按类发现主题,即,跨类共享的单个主题集被多个特定于类的主题集代替。展示了css-LDA模型将主题监督的标签强度与主题发现的灵活性相结合。通过广泛的实验评估(包括多个基准数据集)证明了其有效性,并证明其优于现有的基于LDA的图像分类方法。

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