基于局部特征和隐条件随机场的场景分类方法

针对复杂场景图像分类的难题,提出一种基于局部特征和隐条件随机场的场景分类方法.该方法将图像划分为一系列超像素区域,提取每个区域的局部特征组成观察图像的输入特征向量,并建立基于隐条件随机场的场景分类模型推断图像的场景类别标记,其中每个局部特征对应一个隐变量.训练采用随机梯度上升法估计模型参数.在标准的图像库上进行实验,结果表明,与同类方法相比,场景分类方法取得了更好的分类结果.

Scene Classification Based on Local Feature and Hidden Conditional Random Fields

To solve the problem of complex scene classification, an approach based on local feature and hide conditional random fields (HCRF) is proposed. Firstly, the image is segmented into sets of super-pixels, and then the local features extracted from those regions are used as the characteristic vectors of input image observations. Secondly, the HCRF model is established to infer the scene category of the image, where every local feature has the corresponding latent variable. The parameters of the model could be estimated using random gradient ascent algorithm. On the public image dataset, the test results demonstrate that the proposed approach has better classification results than the previous methods.