# Weakly Supervised Conditional Random Fields Model for Semantic Segmentation with Image Patches
## 2 Related Work
### Image Semantic Segmentation  
In the past years, ISS has attracted much attention and become one of the hotspots of computer vision. Among the existing ISS methods, there are two main categories: fully supervised and weakly supervised semantic segmentation algorithms. The difference between them is that full supervision requires pixel-level label learning, while weak supervision only needs image-level label learning, which greatly reduces the cost of human and material resources caused by manual annotation [26]. It has a good application prospect, although there are many issues to be addressed in weakly supervised semantic segmentation. These weakly supervised semantics segmentation methods can be roughly divided into two categories: traditional methods and deep learning methods. Traditional semantic segmentation needs a process of feature extraction followed by several different classifiers to complete the segmentation. [PERSON] et al. [27] first proposed the concept of Blob-World, and used image-level label training classifier to conduct image semantic segmentation. [PERSON] et al. [28] proposed an effective support vector machine classifier based on spatial sparse reconstruction method. The classifier is trained with noisy data and denoised by subspace reconstruction method. The optimal parameters are obtained by iterative optimization. [PERSON] et al. [29] proposed multi-image model (MIM), using conditional random field model, the one-dimensional potential energy function is established with single superpixel pairs and the two-dimensional potential energy function is established with superpixel pairs. The semantic segmentation result is obtained by CRF parameter approximation solution. [PERSON] et al. [30] proposed weakly-supervised dual clustering for image segmentation and label correspondence. [PERSON] et al. [31] proposed a graph model for recoveringthe pixel based on the appearance similarity of training image superpixels. This model is different from the traditional classifier and has achieved good results when learning multi-class kernel matrices. [PERSON] et al. [32] proposed a probabilistic graph model called TCPR for weakly supervised labeling. This method adds neighborhood context constraint to the MRF model and can use automatic inference mechanism to automatically infer category labels. Deep learning semantic segmentation method generally consists of a general network framework and a segmentation network. The performance improvement of network structure also brings great improvement to the precision of image processing. To solve the problem of feature loss caused by network framework pooling and down-sampling operation, [PERSON] et al. [33] proposed a deconvolution neural network, which combined the prediction method of network and full convolution network to achieve semantic segmentation task. [PERSON] et al. [34] proposed multi-scale convolution neural network based deep learning for semantic segmentation, in which pixel-level features were extracted including texture, shape and context information. [PERSON] et al. [35] proposed a framework to reduce the error in weakly supervised learning with image-level supervision. In this way, semantic segmentation and object localization are unified to improve segmentation performance. [PERSON] et al. [36] proposed a framework for generating localization maps by hypotheses-aware classification and cross image contextual refinement. [PERSON] et al. proposed to refine the pixel-wise prediction from the last DCNN layer with a fully connected CRF and achieved better segmentation results [3]. [PERSON] et al. [37] develop expectation maximization (EM) methods for semantic image segmentation model training under these weakly supervised and semi-supervised settings. [PERSON] et al. [38] proposed a simple to complex (STC) framework, which used simple image-level labels to enhance the Initial-DCNNs network, and then used the Enhanced-DCNNs network to complete more complex ISS tasks.