assignment 6.1 image labeling

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Complete Guide to Image Labeling for Machine Learning

Image labeling enables you to tag and identify specific details in an image. In computer vision, image labeling involves adding specific tags to raw data, including videos and images. Each tag represents a certain object class associated with this data.  Supervised machine learning (ML) models utilize labels to learn to identify a certain object class within unclassified […]

Image labeling enables you to tag and identify specific details in an image. In computer vision, image labeling involves adding specific tags to raw data, including videos and images. Each tag represents a certain object class associated with this data. 

Image annotation is a  type of image labeling  used to create datasets for computer vision models. You can split these datasets into training sets to train ML models and test or validate datasets before using them to evaluate model performance. 

Data scientists and  machine learning engineers  employ these datasets to train and evaluate ML models. At the end of the training period, the model can automatically assign labels to unlabeled data.

Why Is Image Labeling Important for AI and Machine Learning?

Image labeling enables supervised machine learning models to achieve computer vision capabilities. Data scientists use image labeling to train ML models to:

• Label an entire image to learn its meaning
• Identify object classes within an image

Essentially, image labeling enables ML models to understand the content of images. Image labeling techniques and tools help ML models capture or highlight specific objects within each image, making images readable by machines. This capability is crucial for developing functional AI models and improving computer vision. 

Image labeling and annotation enable object recognition in machines to improve computer vision accuracy. Using labels to train AI and ML helps the models learn to detect patterns. The models run through this process until they can recognize objects independently.

Types of Computer Vision Image Labeling

Image classification.

You can annotate data for image classification by adding a tag to an image. The number of unique tags in a database matches the number of classes the model can classify.

Here are the three key classification types:

• Binary class classification:  Includes only two tags
• Multiclass classification:  Includes multiple tags
• Multi-label classification:  Each image can have more than one tag

Image Segmentation

Image segmentation involves using computer vision models to separate objects in an image from their backgrounds and other objects. 

It usually requires creating a pixel map the same size as the image, using the number 1 to indicate the object is present and the number 0 to indicate no annotations exist. 

Segmenting multiple objects in the same image involves concatenating pixel maps for each object channel-wise and using the maps as ground truth for the model.

Object Detection

Object detection involves using computer vision to identify objects and their specific locations. Unlike image classification, object detection processes annotate each object using bounding boxes. 

A bounding box consists of the smallest rectangular segment containing an object in the image. Bounding box annotations are often accompanied by tags, providing each bounding box with a label in the image.

The coordinates of bounding boxes and associated tags are usually stored in a separate JSON file in a dictionary format. Typically, the image number or image ID is the dictionary’s key.

Pose Estimation

Pose estimation involves using computer vision models to estimate a person’s pose in an image. It works to detect key points in the human body and correlate them to estimate the pose, meaning the key points serve as the corresponding ground truth for pose estimation. 

Pose estimation requires labeling simple coordinate data with tags. Each coordinate indicates the location of a certain key point, which is identified by a tag in the image.

Effective Image Labeling for Computer Vision Projects

The following best practices can help you perform more effective image selection and labeling for computer vision models:

• Include both machine learning and domain experts in initial image selection.
• Start with a small batch of images, annotate them, and get feedback from all stakeholder to prevent misunderstandings and understand exactly what images are needed.
• Consider what your model needs to detect, and ensure you have sufficient variation of appearance, lighting, and image capture angles.
• When detecting objects, ensure you select images of all common variations of the object – for example, if you are detecting cars, ensure you have images of different colors, manufacturers, angles, and lighting conditions.
• Go through the dataset at the beginning of the project, consider cases that are more difficult to classify, and come up with consistent strategies to deal with them. Ensure you document and communicate your decisions clearly to the entire team.
• Consider factors that will make it more difficult for your model to detect an object, such as occlusion or poor visibility. Decide whether to exclude these images, or purposely include them to ensure your model can train on real-world conditions.
• Pay attention to quality, perform rigorous QA, and prefer to have more than one data labeler work on each image, so they can verify each other’s work. Mismatched labels can negatively affect  data quality  and will hurt the model’s performance.
• As a general rule, exclude images that are not sharp enough, or do not have enough visual information. But take into account that the model will not be able to work with these types of images in real life. 
• Use existing datasets – these typically contain millions of images and dozens or hundreds of different categories. Two common examples are ImageNet and COCO. 
• Use transfer learning techniques to leverage visual knowledge from similar, pre-trained models and use it for your own models.

In this article, I explained why image labeling is critical for machine learning models related to computer vision. I discussed the important types of image labeling – image classification, image segmentation, object detection, and pose estimation. 

Finally, I provided some best practices that can help you make image labeling more effective, including:

• Include experts in initial image selection
• Start with a small batch of images
• Ensure you capture all common variations of the object
• Consider edge cases and how to deal with them
• Consider factors like occlusion or poor visibility
• Pay attention to quality
• Use existing datasets if possible and use transfer learning to leverage knowledge from similar, pre-trained models for your own models.

I hope this will be useful as you advance your use of image labeling for machine learning.

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Image Labeling by Assignment

Journal of Mathematical Imaging and Vision

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ConfLabeling: Assisting Image Labeling with User and System Confidence

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• First Online: 25 November 2022
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• Chia-Ming Chang 11 &
• Takeo Igarashi 11  

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13518))

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Interactive labeling supports manual image labeling by presenting system predictions for users to fix errors. However, existing labeling methods do not effectively consider image difficulty, which may affect system predictions and user labeling. We introduce ConfLabeling , a confidence-based labeling interface that represents image difficulties as user and system confidence. This interface allows users to give a confidence score to each label assignment (user confidence), and our system visualizes the results of predictions with confidence levels (system confidence). We expect user confidence to improve system prediction, and system confidence would help users quickly and correctly identify the images that need to be inspected. We conducted a user study to compare our proposed confidence-based interface with a conventional non-confidence interface in an interactive image labeling task of varying difficulty. The results indicate that the proposed confidence-based interface achieved higher classification accuracy than a non-confidence interface when the image was not too difficult.

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Acknowledgements

This work was supported by JST CREST Grant Number JP- MJCR17A1, Japan.

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Lu, Y., Chang, CM., Igarashi, T. (2022). ConfLabeling: Assisting Image Labeling with User and System Confidence. In: Chen, J.Y.C., Fragomeni, G., Degen, H., Ntoa, S. (eds) HCI International 2022 – Late Breaking Papers: Interacting with eXtended Reality and Artificial Intelligence. HCII 2022. Lecture Notes in Computer Science, vol 13518. Springer, Cham. https://doi.org/10.1007/978-3-031-21707-4_26

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Title: image labeling by assignment.

Abstract: We introduce a novel geometric approach to the image labeling problem. Abstracting from specific labeling applications, a general objective function is defined on a manifold of stochastic matrices, whose elements assign prior data that are given in any metric space, to observed image measurements. The corresponding Riemannian gradient flow entails a set of replicator equations, one for each data point, that are spatially coupled by geometric averaging on the manifold. Starting from uniform assignments at the barycenter as natural initialization, the flow terminates at some global maximum, each of which corresponds to an image labeling that uniquely assigns the prior data. Our geometric variational approach constitutes a smooth non-convex inner approximation of the general image labeling problem, implemented with sparse interior-point numerics in terms of parallel multiplicative updates that converge efficiently.

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