Building a Visual Search Engine
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Learn to build a visual search engine that allows users to search for images based on visual similarity. Explore computer vision and machine learning techniques to enable intelligent search.
At a Glance
Build a visual search engine based on images. Search for visually close images from a database given a sample query image.
On the streets, you see someone wearing a T-Shirt you really like. You visit an online shopping site later that day, uploading an image of the shirt into the search bar and found an exact match. How is it possible that we don’t need to search by words, but instead can use images now? The “magic” behind this feature is a system for image querying. This guided project will help you build an image querying prototype. The project is split into two parts. The first part focuses on building the image encoding system and the second part focuses on building the image query system.
- Enabling customers to look for similar apparel, furniture, auto parts etc.
- Help eliminate near duplicate images from databases or catalogues.
- Enable image to be used as feature embedding for modeling tasks.
- Build image based recommendation systems.
A Look at the Project Ahead
After completing this Guided Project, you will be able to:
– Setup an Image Encoding service that accepts input images and produces embeddings
What You’ll Need
This course mainly uses Python. Prior experience with libraries such as Tensorflow and PIL are recommended but not required. System requirements for if you run the labs on a local machine include TensorFlow 2.x and 2GB of storage (for storing the dataset).
Frequently Asked Questions
Your Instructors
Kai Niu
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