# Deep Learning Based Semantic Image Segmentation Methods for Classification of Web Page Imagery
## 1 Introduction  
Image segmentation is the process of separating an image into several segments while assigning an item class to each pixel in the image [1]. The two basic types of image segmentation are semantic and instance segmentations. Semantic image segmentation is defined as \"the assignment of class labels for each pixel of an image dependent on the class it belongs to\" [2]. The segmentation of semantic data has been done using traditional approaches such as k-means clustering, thresholding, and histogram-based methods. It has taken deep learning years to push the limits and build alternative architectures that may considerably improve segmentation accuracy. In semantic segmentation, a single class label is used to identify all items of the same type; however, in instance segmentation, related objects are given their own labels. Even though it is a pixel-level classification problem in the pixels of an image, it is a far more challenging technique than traditional classification, which seeks to predict the labels of the complete image on which we trained. Deep learning's enormous success has had a tremendous impact on semantic segmentation algorithms, greatly improving their accuracy. This prospective breakthrough has piqued the interest of several technological and academic sectors that require advanced computer vision skills [3]. Various visual tasks have been brought to the cutting edge of technology using deep convolutional neural networks (DCNNs). Deep neural networks are highly efficient at semantic segmentation or labeling each region or pixel with an object or non-object class. Semantic segmentation is necessary for image analysis tasks and plays a key role in image comprehension. It has numerous applications in computer vision and artificial intelligence (AI), including autonomous driving [4, 5], robot navigation [6], industrial inspection [7]; rehabilitation [8]; document analysis [9]; remote sensing [10, 11]; cognitive and computational sciences--saliency object detection [12]; agriculture sciences [13]; fashion--classifying clothing items [14]; social sciences--marketing [15] and consumer preference analysis [16]; medical sciences--medical imaging analysis [17, 18, 19], etc. Earlier approachesfor semantic segmentation included slope difference distribution [20], support vector machine (SVM) [21], and random-forest-based classifiers [22]; however, deep learning techniques have enabled segmentation that is more exact and substantially faster [23; 24]. Convolutional neural networks (CNNs) offer cutting-edge solutions for a variety of issues in machine learning and computer vision, including image recognition [25; 26], semantic segmentation [27; 28], and object identification [29]. CNNs were first utilized in 1998 [30]. LeNet-5, the first CNN, could read handwritten numbers from pictures. Layers of various types, including convolutional, pooling, and fully connected layers, are present in CNNs. By stacking these layers, CNNs may automatically learn feature representations by applying local neighborhood pooling operations and trainable filters to the resulting feature maps and raw input pictures. This is distinct from conventional pattern recognition models, where relevant data is manually extracted by a feature extractor from the input and then categorized by a trainable classifier. Semantic segmentation methods can be used to categorize web pages, in which website snapshots must comprehend their complete layout structure (html tags), such as p tags for paragraphs, <img> tags for images, menu tags for website buttons, and logo tags for website image logos, among others.