October 2019
Intermediate to advanced
583 pages
14h 36m
English
Content preview from Practical Deep Learning for Cloud, Mobile, and Edge
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Chapter 4. Building a Reverse Image Search Engine: Understanding Embeddings
Bob just bought a new home and is looking to fill it up with some fancy modern furniture. He’s flipping endlessly through furniture catalogs and visiting furniture showrooms, but hasn’t yet landed on something he likes. Then one day, he spots the sofa of his dreams—a unique L-shaped white contemporary sofa in an office reception. The good news is that he knows what he wants. The bad news is that he doesn’t know where to buy it from. The brand and model number is not written on the sofa. Asking the office manager doesn’t help either. So, he takes a few photos from different angles to ask around in local furniture shops, but tough luck: no one knows this particular brand. And searching on the internet with keywords like “white L-shaped,” “modern sofa” gives him thousands of results, but not the one he’s looking for.
Alice hears Bob’s frustration and asks, “Why don’t you try reverse image search?” Bob uploads his images on Google and Bing’s Reverse Image Search and quickly spots a similar-looking image on an online shopping website. Taking this more perfect image from the website, he does a few more reverse image searches and finds other websites offering the same sofa at cheaper prices. After a few minutes of being online, Bob has officially ordered his dream sofa!
Reverse image search (or as it is more technically known, instance retrieval) enables developers and researchers to build scenarios beyond simple ...
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