Using Data Analysis To Design Better Photos
Better cameras don’t necessarily take photos that people think are better.
The visual image criteria that customers respond favorably to is an ongoing area of interest to me. I routinely use knowledge gained through A/B testing of images to achieve competitive advantage by producing images specifically designed to appeal to specific users.
In a previous article I discussed how Netflix uses image analysis to create visual content.
This data analysis reveals trends in color preferences and style for images used at Netflix.
Cameras get better each year with sensors capable of capturing more pixels, and more dynamic range. The result are images that are sharp, and have more details in shadows and highlights than ever before.
Advances in Computational Photography allow cell phone cameras to automatically take “better photos” without extra effort and knowledge of photography. Throwing processing power at raw images lets smartphones and cameras do some amazing things. Computational Photography tweaks settings between each shot ensuring that people and scenes always “look their best”.
At least that is the claim.
The problem is that better cameras don’t necessarily take photos that the majority of people prefer because they don’t take trends in color preference and style into consideration.
Marques Brownlee has a popular channel on Youtube where he reviews tech products. He created a video called “The Blind Smartphone Camera Test 2018!” which revealed that “better cameras” are not capturing and processing photos that the vast majority of people prefer to the images captured by less capable cameras.
P = Xiaomi Pocophone F1
Image Credit: Marques Brownlee
He used 16 of the most popular mobile phones in a winner takes all bracket style competition. He included powerful phones like the Pixel 3, Hydrogen, and the iPhone X, and iPhone XS along with less capable cameras used by the Blackberry and Xiaomi Pocophone.
Most images are viewed on social media platforms now, and it is well known that Twitter and Instagram have horrible image compression, but it makes sense to use these platforms to test trends and image preferences.
The results revealed an obvious trend. People do not like the perfectly exposed high dynamic range photos that the “best cameras” captured.
There’s obviously a massive subjective component: people like brighter, warmer, punchy photos.
Computation photography and large image sensors capable of capturing copious amounts of detail and high dynamic range are still not a substitute for good glass, and an artistic understanding of photographic principals and composition.
In the end, you have to understand what your customers prefer, and craft visual strategies designed to appeal to those preferences.
It really is not that much of a blow for Google to shutter Google+. Google admits that Google+ has “low usage and engagement” and that 90 percent of Google+ user sessions last less than five seconds.
If you are not familiar with the search engine DuckDuckGo yet, you will probably be hearing more about it from now on. DuckDuckGo is a search engine, like Google, Yahoo or Bing, but with DuckDuckGo your searches and your IP address are kept 100% anonymous. People seeking ways to reduce their digital footprint online is driving the rapid traffic growth at DuckDuckGo.com.
The human brain consists of 100 billion cells called neurons. The neurons are connected together by synapses. If sufficient synaptic inputs to a neuron fire, that neuron will also fire. This process is called “thinking”.
Keras is an open source neural network library written in Python. Keras was conceived to be an interface rather than a standalone machine-learning framework. It offers a higher-level, more intuitive set of abstractions that make it easy to develop deep learning models regardless of the computational backend used. It is capable of running on top of 
TensorFlow was originally developed by the Google Brain Team within Google’s Machine Intelligence research organization for machine learning and deep neural networks research.
Artificial Intelligence and Machine Learning are shaping many of the products and services you interact with every day. In future blog posts I will be discussing how Artificial Intelligence, Machine Learning, Neural Networks, and Predictive Analytics are being used by Marketers to achieve competitive advantage.
According to Pew Research Facebook users continue to reduce the amount of time they are spending on the platform. Just over half of Facebook users ages 18 and older (54%) say they have adjusted their privacy settings in the past 12 months, according to a new Pew Research Center survey. Around four-in-ten (42%) say they have taken a break from checking the platform for a period of several weeks or more, while around a quarter (26%) say they have deleted the Facebook app from their cellphone. All told, some 74% of Facebook users say they have taken at least one of these three actions in the past year.
There are, however, age differences in the share of Facebook users who have recently taken some of these actions. Most notably, 44% of younger users (those ages 18 to 29) say they have deleted the Facebook app from their phone in the past year, nearly four times the share of users ages 65 and older (12%) who have done so. Similarly, older users are much less likely to say they have adjusted their Facebook privacy settings in the past 12 months: Only a third of Facebook users 65 and older have done this, compared with 64% of younger users. In earlier research, Pew Research Center has found that a larger share of younger than older adults use Facebook. Still, similar shares of older and younger users have taken a break from Facebook for a period of several weeks or more.
There are variety of good reasons to use open source technology when creating solutions for your business.
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