Semantic Image Segmentation Using Deep Learning
What is Deep Learning ?
Deep Learning is considered as a part of machine learning and AI that copies or imitates the way humans learn and gain knowledge. Simple and traditional machine learning algorithms are linear in nature, but in deep learning, the algorithms are stacked in a hierarchy of increasing abstraction and complexity.
What do you mean by Semantic Segmentation ?
Semantic Segmentation is a process of cooperation for each and every pixel of an image with a class label. For example, a flower, a person, road, sky, car, ocean, etc. One of the applications is Autonomous Driving. Semantic segmentation gains fine grade interference by making some dense predictions by interfering labels for each and every pixel. This is done in this way so that each and every pixel is labeled with the class.
Now, let us talk about some tools that are used as the basis of semantic segmentation :
● VGG-16 : It is an Oxford model that won the ImageNet competition with an accuracy of 92.7 % in the year 2013. It applies a stack of convolution layers with some small receptive fields especially in the first layer rather than in some few layers having big receptive fields.
● GoogLeNet : It is a google’s network with an accuracy of 93.3 % who won the ImageNet competition in the year 2014. It has 22 layers and a building block that has been newly introduced that is called an inception module. The inception module consists of a Network in Network layer that is a poling operation, a small and a large sized convolution layer.
● Resnet : It is a semantic segmentation model given by Microsoft, that won the ImageNet competition in the year 2016 and had an accuracy of 96.4 %. It is a very well known model because of its depth that is 152 layers and the introduction of residual blocks. These blocks address the real problem of training deep architectures by the introduction of identity skip connections. It is so that the layers are able to copy their inputs to the next layer.
These were some of the models used in common. Now, let us discuss the approaches to semantic segmentation.
Approaches to Semantic Segmentation :
Well, the architecture can be thought as :
● The encoder : It is a pre-trained classification network just like the VGG/ResNet network that is then followed by a decoder network.
● The decoder : Now comes to part of the decoder. The work of the decoder is to project the discriminative features semantically that are then learnt by the encoder in the pixel space so as to get a dense classification of the images.
- Region – Based Semantic Segmentation : It follows a method known as segmentation using recognition. First, it extracts the free form regions from an image and after doing this work it describes them. It is then followed by region based classification. At the time of testing, the region based predictions are then transformed to the pixel predictions. It is done by labelling the pixels according to the highest scoring region that it is containing.
- Convolutional – Based Semantic Segmentation : In this approach, the model learns mapping from step to step, like pixels to pixels without even extracting the region proposals. Its main idea is to make CNN as an input arbitrary sized image. Nowadays, a variety of more advanced FCN-based are being proposed and applied like, SegNet, DeepLab, and also Convolutions.
- Weakly Supervised Semantic Segmentation : It is considered as one of the relevant methods in the semantic segmentation process. It is done by using annotating boces around the images.
Fully Convolutional Network has some key features in the architecture. They are :
● FCN works to transfer the knowledge from the VGG16 so as to perform the semantic segmentation process.
● The layers are connected to VGG16 and then converted to fully convolutional layers by using the 1 * 1 convolution.
● At each and every stage, the upsampling process is then refined by adding different features from the coarser, i.e. higher coarser maps from lower layers of the VGG16.
● The skip connection is then introduced after each and every convolution block so as to enable subsequent blocks to obtain more abstract.
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