The New World of Machine Photo Recognition



Red DogBack in 2012 Google announced that its computers learned to recognize pictures of cats.

Technology doesn’t stand still. Today in 2016 Google’s computers can now recognize many other things besides disinterested feline faces. What comes from this machine recognition ability goes far beyond facial recognition.

About a year ago, I uploaded my entire library of photos and many of my videos to Google Photos as a supplemental photo backup system. I am using the free version, allowing Google to compress images as they see fit to save space. Over a period of time my thousands of scanned 35mm negatives finally uploaded. I install Google Photos on all of my mobile devices so any photos or videos I take will automatically be uploaded.

I was looking at Google Photos as just a convenient photo/video backup insurance plan. As it turns out, I’m getting a lot more than I bargained for. The ability to search my personal photo collection is nothing short of remarkable.

Who has the time to enter keywords into photo management systems? Like most other people with large digital photo collections, I’ve never bothered.

Machine learning now renders labor-intensive keyword entry unnecessary.
Though the current level of machine learning isn’t perfect, it is more than good enough to be extremely useful. It is now possible for me to quickly find specific photos or types of photos from my massive lifetime image library.

For example, I take search for “zoo” and photos of animals in cages plus various animals that can be found in zoos pops up. If I type in “rodeo” a massive number of rodeo photos will pop up, not surprising since I spent years photographing my youngest brother, who used to be a rodeo bull rider.

If I search for “dog” all manner of dog photos I’ve taken over the years show up.

If I search for “cup” all kinds of photos will show up, including photos of different types of cups, people holding cups, etc.

If I type in people’s names, chances are I will get photos of them. I’m not surprised by this since when I scanned in a number of old print photos I named the files with the names of the people in them.

Searching on adjectives such as “cold” or “hot” may or may not yield image results. Using search words such as “blue” or “green” can result in images that have blue or green objects in them, or they may have an overall blue or green cast to them, such as images taken without flash under different colors of florescent lights.

Over time the machine learning is bound to improve and the search results will become even better than they already are.


4 thoughts on “The New World of Machine Photo Recognition

  1. Great article Tom.
    How much space fb you get ? My files are massive. Can you think of a way low res photios could help me keep track. I have not done keywords but do have collections.

  2. Yes. Here’s how. Upload all of your photos to Google Photos allowing Google to compress the backups. Once they are uploaded, you can search the uploaded files taking advantage of their machine learning image recognition. When you find a photo you are searching for and want to find the corresponding larger-sized image, click on the photo and then on the “info” symbol in the upper right corner of the browser window. It will tell you the name of the file. You can then search your local drive for the file. In most cases, I find that I don’t care so much about finding the larger files, because the uploaded compressed version is more than good enough to share online.

    Google does offer additional storage for a monthly fee. They offer 100 gigabytes for $1.99 per month, and one terabyte for $9.99 per month.

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