03/04/2020 – Pull the Plug? Predicting If Computers or Humans Should Segment Image – Yuhang Liu

Summary:

This paper examines a new image segmentation method. Image segmentation is a key step in any image analysis task. There have been many methods before, including low-efficiency manual methods and automated methods that can produce high-quality pictures, but these methods have certain disadvantages. The authors therefore propose a distribution framework that can predict how best to assign fixed labor to collect higher quality segmentation for a given image and automated method. Specifically, the author has implemented two systems, which can perform the following processing on images when doing image segmentation:

1. Use computers instead of humans to create the rough segmentation needed to initialize the segmentation tool,
2. Use computers to replace humans to create the final fine-grained segmentation. The final experiments also proved that relying on this hybrid, interactive segmentation system can achieve faster and more efficient segmentation.

Reflection:

Once, I did a related image recognition project. Our subject is a railway turnout monitoring system based on computer vision, which is to detect the turnout of the railroad track from the picture, and the most critical step is to separate the outline of the railroad track. At that time, we only using the method of computer separation, the main problem we encountered at the time was that when the scene became complicated, we would face to complex line segments, which would affect the detection results. As mentioned in this paper, using human-machine, the combined method can greatly improve the accuracy rate. I very much agree with it, and hope that one day I can try it myself. At the same time, what I most agree with is that the system can automatically assign work instead of all photos going through a same process. For a photo, only the machine can participate, or artificial processing is required. This variety of interactive methods, It is far more advantageous than a single method, which can greatly save workers’ time without affecting accuracy, and the most important point is that complex interaction methods can adapt to process more diverse pictures. Finally, I think similar operations can be applied to other aspects. This method of assigning tasks through the system can coordinate the working relationship between humans and machines, for example, in other fields, such as sound sentiment analysis and musical background separation. In these aspects, humans have the incomparable advantages of machines and can achieve good results, but it takes a long time and is very expensive. Therefore, if we can classify this kind of thinking, deal with the common working relationship between humans and machines, and give complex situations to people or pass the rough points of the machine first, then the separation cost will be greatly reduced, and the accuracy rate will not be affected, so I It is believed that this method has great application prospects, not only because of the many application directions of image separation, but we can also learn from this idea to complete more detailed analysis in more fields.

Question:

1. Is this idea of cooperation between man and machine worth learning?
2. Because the system defines the working range of people and machines, will the machine reduce the accuracy due to the results of human work?
3. Does man-machine cooperation pose new problems, such as increasing costs?