Supervised machine learning is a subcategory of artificial intelligence, which uses labeled data to carry out machine learning tasks. In computer vision applications, labels on the images (metadata, descriptions, etc.) are used to train the model, which then uses this information to determine what is in the image.
The basic objective of such models is to precisely anticipate the desired outcome for unknown data. In a variety of industry use cases that allow businesses to use data to enhance proper results in their operations, supervised machine learning is by far the most used technique. For example, this is evident through image recognition in retail.
One thing is clear, to provide fruitful results the labeled data at hand is crucial to the model’s efficacy.
To train a well-performing machine learning model, vast amounts of data accurately representing real-world examples are required, usually consisting of hundreds and thousands of images. Manual labeling and quality assurance require a substantial human workforce, it being a tedious and resource-heavy process. Besides that, labeling the dataset comes with other obstacles:
Challenges such as the quality and amount of data may damage the model learning resulting in the insufficient performance of the final model, while the rest of the challenges require the team to spend extra time on the computer vision task and delay its finish.
One of the solutions to tackle labeling challenges is to automate the image annotation process.
SentiSight.ai classic AI-assisted image labeling requires the user to label a small data sample, train a model on this data, and then use this model to predict labels for the rest of the dataset.
Additionally, this technique requires manual human intervention for quality assurance to double-check that the model has labeled the dataset correctly. This creates great accuracy since it was trained with specific requirements in mind working on a particular task, although it does require extra manual steps at the beginning of the task and during quality assurance.
In order to provide more help and support in image annotation tasks, SentiSight.ai provides an option to label images by similarity.
Image similarity is a measurement to evaluate how much several pictures are alike. It addresses the issue of locating objects that are the most similar to or closest to the input data within big datasets that typically do not have a natural order of items.
Its applications include hierarchical data clustering and analysis, near-duplicate detection, and the development of recommendation systems, among other use cases.
Image similarity measurement can be applied to AI-assisted image labeling to improve efficiency. Such a labeling technique requires minimal manual preparation:
This technique is more efficient than the classic AI-assisted image labeling since the similarity model does not need to be trained beforehand. Even if the dataset consists of niche class objects, since the ground truth data sample is provided, labeling by similarity will offer predictions despite having just one image per label. The labeling process is sped up by reviewing suggested labels, which is usually quicker than labeling them from scratch.
Labeling by Similarity can be performed either on already existing pictures or by uploading new images to the project.
There are a few parameters that shall be adjusted for the best model performance.
If you are unsure about the parameter selection, do not hesitate to contact us for a consultation based on your custom project needs.
To sum up, AI-assisted Image Labeling by Similarity offers a convenient, fast, and efficient way to label a vast dataset to train a machine learning model by yourself without needing to hire a team of image annotators. It can be used for iterative or AI-assisted image labeling as well as single-label or multi-label image classification predictions.
This labeling technique reduces financial strain and improves the delivery time of the projects, allowing our users to spend more time on more pressing matters. Start building your dataset with the help of the Label by Similarity tool on the SentiSight.ai platform today!