View source for 18555136. METHOD AND SYSTEM FOR PROVIDING LABELED IMAGES FOR SMALL CELL SITE SELECTION simplified abstract (Telefonaktiebolaget LM Ericsson (publ))

=METHOD AND SYSTEM FOR PROVIDING LABELED IMAGES FOR SMALL CELL SITE SELECTION=


==Organization Name==

[[:Category:Telefonaktiebolaget LM Ericsson (publ)|Telefonaktiebolaget LM Ericsson (publ)]]


[[Category:Telefonaktiebolaget LM Ericsson (publ)]]

==Inventor(s)==

[[:Category:Soufiane Belharbi of Montreal (CA)|Soufiane Belharbi of Montreal (CA)]][[Category:Soufiane Belharbi of Montreal (CA)]]

[[:Category:Eric Granger of Montreal (CA)|Eric Granger of Montreal (CA)]][[Category:Eric Granger of Montreal (CA)]]

[[:Category:Aydin Sarraf of Pierrefonds (CA)|Aydin Sarraf of Pierrefonds (CA)]][[Category:Aydin Sarraf of Pierrefonds (CA)]]

==METHOD AND SYSTEM FOR PROVIDING LABELED IMAGES FOR SMALL CELL SITE SELECTION - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18555136 titled 'METHOD AND SYSTEM FOR PROVIDING LABELED IMAGES FOR SMALL CELL SITE SELECTION

The disclosure pertains to a method for training a model to label images, involving obtaining a dataset of labeled images, selecting a second dataset of images for pseudo labeling, feeding these images into the model, combining the datasets, training the model, and testing it for performance.

* The method involves using a first dataset of labeled images selected from a pool of images showing different views of geographical areas.
* A second dataset of images is chosen from the pool for pseudo labeling, distinct from the first dataset.
* The second dataset is fed into the model to obtain pseudo-labels for the images.
* The first dataset and the second dataset with pseudo-labels are combined into a third dataset for training the model.
* The model is tested with a fourth dataset of labeled images, and if the desired performance is achieved, the model is stored; otherwise, the method is repeated.

Potential Applications:
- This method can be applied in various industries such as autonomous vehicles, surveillance systems, and satellite imagery analysis.
- It can enhance the accuracy and efficiency of image labeling tasks in computer vision applications.

Problems Solved:
- Addresses the challenge of training models for image labeling with limited labeled data.
- Improves the performance of models by incorporating pseudo-labeling techniques.

Benefits:
- Increases the accuracy of image labeling models.
- Enhances the generalization capabilities of models by leveraging pseudo-labeling.
- Optimizes the training process by combining labeled and pseudo-labeled datasets.

Commercial Applications:
- This technology can be utilized in industries such as agriculture, urban planning, and environmental monitoring for efficient image analysis and labeling tasks.

Prior Art:
- Researchers have explored the use of pseudo-labeling techniques in machine learning to improve model performance with limited labeled data.

Frequently Updated Research:
- Stay updated on advancements in pseudo-labeling techniques for image labeling tasks to enhance model performance and efficiency.

Questions about Image Labeling with Pseudo-Labeling:
1. How does pseudo-labeling improve the training process of image labeling models?
   - Pseudo-labeling helps incorporate unlabeled data into the training process, enhancing model performance and generalization.
2. What are the potential challenges associated with using pseudo-labeling in image labeling tasks?
   - Some challenges include ensuring the quality of pseudo-labels and managing the integration of labeled and pseudo-labeled datasets effectively.


==Original Abstract Submitted==

The disclosure relates to a method for training a model for labeling images. The method comprises obtaining a first dataset of labeled images, the first dataset of labeled images being selected from a pool of images presenting different views of portions of geographical areas; selecting, from the pool of images, a second dataset of images for pseudo labeling, different from the first dataset; feeding the second dataset of images into the model and obtaining as output of the model the second dataset of images with pseudo-labels; combining the first dataset of labeled images with the second dataset of images with pseudo-labels into a third dataset; training the model with the third dataset; and testing the model with a fourth dataset of labeled images and, upon determining that a requested performance is met, storing the model, or, upon determining that the requested performance is not met, executing the method again.



[[Category:G06F18/214]]
[[Category:G06T7/11]]
[[Category:G16H30/40]]
[[Category:CPC_G06F18/214]]