Adaptive Scene Category Discovery With Generative Learning and Compositional Sampling
Adaptive Scene Category Discovery With Generative Learning and Compositional Sampling
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DOI：
10.1109/tcsvt.2014.2313897
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发表时间：
2015-02
影响因子：
8.4
通讯作者：
Liang Lin;Ruimao Zhang;Xiaohua Duan
中科院分区：
文献类型：
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作者：
Liang Lin;Ruimao Zhang;Xiaohua Duan
研究了一种根据场景图像的外观(即纹理和结构)来发现未标记场景图像类别的通用框架。我们以一种无监督的方式共同解决这两个耦合任务：1)在不预先确定类别数量的情况下对图像进行分类；2)为每个类别寻求生成模型。在我们的方法中，每幅图像由两种类型的图像描述符表示，这两种类型的图像描述符可以有效地从不同的方面捕捉图像外观。通过将每幅图像看作一个图顶点，建立一个图，并将图像分类看作一个图的划分过程。具体地说，一个划分的子图可以看作是一个场景范畴，我们通过累加所有分开的范畴的生成模型来定义图划分的概率模型。为了有效地利用图进行推理，我们采用了一种基于Metropolis-Hasting机制设计的随机聚类抽样算法。在推理的迭代过程中，通过产生式学习算法对每个类别的模型进行分析更新。在实验中，我们的方法在几个具有挑战性的数据库上得到了验证，并且它的性能优于其他流行的最先进的方法。文中还给出了实现细节和实证分析。
This paper investigates a general framework to discover categories of unlabeled scene images according to their appearances (i.e., textures and structures). We jointly solve the two coupled tasks in an unsupervised manner: 1) classifying images without predetermining the number of categories and 2) pursuing generative model for each category. In our method, each image is represented by two types of image descriptors that are effective to capture image appearances from different aspects. By treating each image as a graph vertex, we build up a graph and pose the image categorization as a graph partition process. Specifically, a partitioned subgraph can be regarded as a category of scenes and we define the probabilistic model of graph partition by accumulating the generative models of all separated categories. For efficient inference with the graph, we employ a stochastic cluster sampling algorithm, which is designed based on the Metropolis-Hasting mechanism. During the iterations of inference, the model of each category is analytically updated by a generative learning algorithm. In the experiments, our approach is validated on several challenging databases, and it outperforms other popular state-of-the-art methods. The implementation details and empirical analysis are presented as well.