New Algorithm From Brown University Researchers Allows You To Change Season, Weather In Photos
Kevin Stacey, Brown University
A computer algorithm being developed by Brown University researchers enables users to instantly change the weather, time of day, season, or other features in outdoor photos with simple text commands. Machine learning and a clever database make it possible. A paper describing the work will be presented at SIGGRAPH 2014.
We may not be able control the weather outside, but thanks to a new algorithm being developed by Brown University computer scientists, we can control it in photographs.
The new program enables users to change a suite of “transient attributes” of outdoor photos — the weather, time of day, season, and other features — with simple, natural language commands. To make a sunny photo rainy, for example, just input a photo and type, “more rain.” A picture taken in July can be made to look a bit more January simply by typing “more winter.” All told, the algorithm can edit photos according to 40 commonly changing outdoor attributes.
The idea behind the program is to make photo editing easy for people who might not be familiar with the ins and outs of complex photo editing software.
“It’s been a longstanding interest on mine to make image editing easier for non-experts,” said James Hays, Manning Assistant Professor of Computer Science at Brown. “Programs like Photoshop are really powerful, but you basically need to be an artist to use them. We want anybody to be able to manipulate photographs as easily as you’d manipulate text.”
A paper describing the work will be presented next week at SIGGRAPH, the world’s premier computer graphics conference. The team is continuing to refine the program, and hopes to have a consumer version of the program soon. The paper is available at http://transattr.cs.brown.edu/. Hays’s coauthors on the paper were postdoctoral researcher Pierre-Yves Laffont, and Brown graduate students Zhile Ren, Xiaofeng Tao, and Chao Qian.
Editing by machine learning
Changing the weather in a photo involves much more than simply turning a blue sky gray. There are subtle changes in color and contrast that happen across the entire photo — changes that would normally require a skilled photo editor to fully replicate. This new algorithm uses machine learning to make all those subtle changes in one swoop.
To start the project, Hays and his team defined a list of transient attributes that users might want to edit. They settled on 40 attributes that range from the simple — cloudy, sunny, snowy, rainy, or foggy — to the subjective — gloomy, bright, sentimental, mysterious, or calm.
The next step was to teach the algorithm what these attributes look like. To do that, the researchers compiled a database consisting of thousands of photos taken by 101 stationary webcams around the world. The cameras took pictures of the same scenes in varying of conditions — different times of day, different seasons and in all kinds of weather. The researchers then asked workers on Mechanical Turk — a crowdsourcing marketplace operated by Amazon — to annotate more than 8,000 photos according to which of the 40 attributes are present in each. Those annotated photos were then fed through a machine learning algorithm.
“Now the computer has data to learn what it means to be sunset or what it means to be summer or what it means to be rainy—or at least what it means to be perceived as being those things,” Hays explained.