Semantic segmentation annotation tools are excellent investments, as they can drastically reduce the amount of time spent preparing images for input into a deep neural network.
Semantic segmentation is the process of associating every pixel, within a region of an image that has semantic value, with a specific label. Semantic segmentation is a powerful tool for deep learning, as it makes images easier to analyze by assigning parts of the image semantic definitions. While semantic segmentation is an incredibly useful technique in deep learning, it can be very difficult to carry out. The images first need to be annotated, and this is best done with a semantic segmentation annotation tool.
Before we go over different tools for semantic segmentation, let’s make sure we understand what semantic segmentation is.
There are different levels of analysis/inference for computer vision systems. There’s low-level analysis and high-level analysis. At low levels of analysis, the computer vision system analyzes individual pixels in the image, but at high levels of analysis, the system draws inferences about different regions/objects in the image.
For instance, classification is done at a lower level of analysis, simply making a prediction about the content of an entire image. After this, a middle level of analysis could be detection/localization, where the object isn’t merely classified, but spatial information about the object is obtained. Semantic segmentation is a high-level task that enables the computer vision system to look at an image and recognize objects or regions by their semantic meaning, inferring a label for every pixel in the object or region. Semantic segmentation enables many different applications of computer vision like virtual reality and self-driving vehicles.
There are different approaches used to carry out semantic segmentation. The common semantic segmentation approaches are: Region-based semantic segmentation, Fully Convolutional Network-Based Semantic Segmentation, and Weakly Supervised Semantic Segmentation.
Region-based semantic segmentation tactics typically use a segmentation method that combines region extraction and semantic-based classification. First, the free-form regions are selected by the model and then these regions are transformed into predictions at a pixel level.
One specific framework used to accomplish this is the Regions with CNN framework, or R-CNN, which uses a selective search algorithm to pull many possible region proposals from an image. It then runs them through a Convolutional Neural Network, pulling features from every one of these different regions. Finally, every region is classified using a linear Support Vector Machine specific to the chosen classes.
The R-CNN extracts 2 different feature types for every region selected by the model. A foreground feature and a full region feature are selected. When these two region features are joined together, the performance of the model improves. While R-CNN models are able to make use of discriminative CNN features and attain improved classification performance, they are also limited when it comes to generating precise boundaries.
A Fully Convolutional Network functions by creating a map that translates pixels to pixels. Unlike the R-CNN described above, region proposals are not created. A Fully Convolutional Network can take arbitrarily sized images as inputs for the network, whereas classical Convolutional Neural Networks can only take in and create labels for inputs of pre-defined sizes, which happens as a result of the fully-connected layers being fixed in their inputs.
However, while FCNs can interpret arbitrarily sized images, they function by running the inputs through alternating convolution and pooling layers, and often times the final result of the FCN is predictions that are low in resolution. This low resolution can make for blurry boundaries between objects.
Most of the commonly used semantic segmentation models have to have a large number of images, each segmented pixel-wise. Creating manual annotations for each of these masks is expensive and time-intensive. Some weakly supervised methods can be used to expedite the process of annotating images, carrying out semantic segmentation by using annotated bounding boxes.
There are different methods for using bounding boxes. BoxUp is a technique that uses bounding boxes to supervise the training of the network and make iterative improvements to the estimated positioning of the masks. Simple Does It is a similar method for using bounding boxes to create masks, but it recursively trains to eliminate noise in the image. Pixel-level Labeling is a technique that gives weights to important pixels in the image, which assists in the task of image-level classification.
By far the most common method for doing semantic segmentation is using an FCN. Fully Convolutional Networks can be implemented by taking a pre-trained network and then just customizing aspects of the network to fit your needs.
Before you annotate your images with semantic segmentation, you should prepare your dataset. This involves making sure that the classes in your dataset have roughly the same amount of images. The classifier will learn to distinguish classes the best if all the classes have approximately the same weight to them. If this is not possible, and the dataset itself has large disparities in its representation of classes, then when training the classifier the images should be stratified to achieve more unanimous representation.
Images that are extremely blurry should also be removed from the dataset as these can confuse the classifier and make both image annotation and training of the CNN difficult. You should also consider if semantic segmentation is really the best annotation method for your needs. Depending on the circumstances, instance segmentation may work better, or you may be able to achieve your goal with just bounding boxes. Instance segmentation not only distinguishes between regions with semantic meaning, but it also distinguishes individual instances of an object.
While semantic segmentation tools can prove useful for your company's computer vision tasks and systems, there is more that goes into semantic segmentation and image annotation than simply having the correct tool. You need knowledge of how to use the tool properly, as well as knowledge of how to navigate the many complex situations that occur when annotating images. For this reason, it is often a good idea to hire professional image annotators, as they will save you a lot of time, stress, and money.