Exploratory Data Analysis on Image Data

SUMMARY

Exploratory Data Analysis (EDA) is a crucial step in understanding your data before applying complex algorithms or models. But how do you approach EDA when working with image data?

In this lesson, you’ll learn what EDA for image data involves—examining the content, structure, and patterns in your images before using them for analysis or machine learning. We’ll explore common issues in image datasets, such as missing or corrupted files, varying image resolutions, inconsistent lighting conditions, and noise, and discuss the appropriate EDA techniques for detecting these issues.

You’ll also get hands-on experience with Python, executing EDA techniques to analyze a given image dataset. By the end of this lesson, you’ll be able to perform a comprehensive exploratory analysis of your image data, uncovering hidden issues and ensuring your dataset is ready for effective analysis and model development.

About The Author

Dilek Dustegor is a Professor of Computing Science at the University of Groningen in the Netherlands. She is interested in bridging the gaps between research, development and implementation using AI and automation. She is pursuing research about modeling, design and analysis of large scale / networked systems using Internet of Things (IoT) and Machine Learning (ML) techniques, with a special interest in smart city applications. She is a seasoned educator, and loves using the newest educational technologies for an enhanced learning experience.