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<aside> 🔑 This guide is meant for users to understand the concept of Image Similarity, how can we use this capability to automate the repetitive tasks to make the data-preparation pipeline more faster, efficient, accurate & convenient .

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Topics Covered

• What is Image Similarity.

• Image Similarity at Labellerr.

• Image Similarity use-cases in Data Preparation.

• Try Image Similarity at Labellerr .

IMAGE SIMILARITY

Image similarity is the measure of how similar two images are. In other words, It quantifies the degree of similarity between intensity patterns in two images.

Similar Images

Source:- https://apple.github.io/turicreate/docs/userguide/image_similarity/images/similar_images.png

IMAGE SIMILARITY & DATA PREPARATION

Data preparation is the process of transforming raw data so that data scientists and analysts can run it through machine learning models to uncover insights or make predictions . There are different steps in data preparation like, Data-Collection, Data-Curation & Data-Annotations. These steps are very time-consuming and requires a lots to resources and cost for doing them on scale, which can be automate or reduced by the capability of IMAGE SIMILARITY . Following is some use-cases where we can use the capability of IMAGE SIMILARITY to automate the tasks and make the process faster .

Source:- https://www.lightly.ai/post/data-preparation-tools-for-computer-vision-2021

1. Data-Collection:- Collecting data for training the ML model is the basic step in the machine learning pipeline. The predictions made by ML systems can only be as good as the data on which they have been trained.

Source :- https://miro.medium.com/max/786/1*IY6m_GAnQvZJrWrEZTtdPQ.png

Following are some of the problems that can arise in data collection:-

• Inaccurate data. The collected datasets could have some data/files that are unrelated to the problem statement. After finding one such file that is inaccurate as per the problem statement we can find out all similar files and remove them from datasets .
• Data imbalance. Some classes or categories in the data may have a disproportionately high or low number of corresponding samples. As a result, they risk being under-represented in the model. We can use the IMAGE SIMILARITY capability to find out the disproportion via finding out the similar images and then accordingly collect more data or remove data to ensure the proportion .