# Deep Learning Based Semantic Image Segmentation Methods for Classification of Web Page Imagery
## 3 Methodology
### Background: Image Segmentation Architectures  
An encoder and a decoder form the foundation of the image segmentation architecture. The encoder uses filters to extract features from the image. The decoder oversees the production of the final output, which is often a segmentation mask containing the object's shape. When it comes to semantic segmentation, what we are trying to predict is an output mask, so this mask will have the same width and height as our input image, and the depth of this mask is going to be equal to the number of classes. This architecture, or a version of it, can be found in almost all architectures. Fully convolutional networks (FCNs) are a variation of CNNs that has made considerable strides in image segmentation. The last fully connected layer of the CNN is converted into a convolution layer for the FCN. Next, a nonlinear filter is applied to the output vectors of each layer. The network may accept inputs of any size and generate outputs with the same spatial dimensions as the inputs. In addition, the classification network may provide a heatmap of the item class in question. Adding network layers and a spatial loss produces a machine that is effective for end-to-end dense learning. FCNs can efficiently learn to make dense predictions for per-pixel tasks such as semantic segmentation. Dense feedforward computation and backpropagation are used to conduct learning and inference in the whole image. In-network upsampling layers allow for pixel-by-pixel prediction and learning in sub-sampled pooling networks. This solution is both asymptotical and efficient and eliminates the need for the complexities found in earlier research. Patch-wise training is inefficient compared to fully convolutional training. Our method does not employ pre- or post-processing complexities, such as superpixels [39], suggestions [40], or post-hoc refining by random fields or local classifiers [41]. Our model uses dense prediction by reinterpreting classification nets as completely convolutional and fine-tuning from their learned representations. In contrast, previous research has employed small convnets without pre-training supervision. When it comes to learning dense predictions, FCNs are more versatile since they can take in pictures of varied sizes, but they are also more efficient because of the upsampling that takes place in the network. An important feature of the FCN is its ability to retain spatial information from the input, which is essential for semantic segmentation since this job involves both localization and categorization. Convolutions with no padding are used to lower the resolution of an FCN's output picture, even if the input image may be of any size. These were developed to reduce the size of filters and the associated computational burden. Consequently, the output is coarse and has been decreased in size by the pixel stride ratio of the output units. There is an inherent contradiction between semantics and location in semantic segmentation: global information resolves the 'what' question, while local information resolves the question of 'where'. The UNet architecture is a variant of the FCN that is often used for semantic segmentation. UNets have the advantage over regular CNNs in that their output includes both localization and classification. In this instance, localization denotes the assignment of a class to each pixel in an image. UNets are superior to FCNs because they need fewer training images and give more precise segmentations. By producing upsampling layers with various feature channels, the layers with a greater resolution may get context information. When working with satellite data, it is crucial not to sacrifice localization and contextual information. UNet is similar to FCN-8 in that it is an encoder and decoder model.