"It's also motivated by a practical idea: that we can work with a given outfit to make small changes so it's just a bit better," said Kristen Grauman, Professor from University of Texas at Austin.
Suggestions may include tweaks such as selecting a sleeveless top or a longer jacket, said researchers.
The tool, named Fashion++, uses visual recognition systems to analyse the colour, pattern, texture and shape of garments in an image.
It considers where edits will have the most impact. It then offers several alternative outfits to the user.
Fashion++ was trained using more than 10,000 images of outfits shared publicly on online sites for fashion enthusiasts.
"Finding images of fashionable outfits was easy," said researcher Kimberly Hsiao. Finding unfashionable images proved challenging. So, she came up with a workaround.
She mixed images of fashionable outfits to create less-fashionable examples and trained the system on what not to wear. "As fashion styles evolve, the AI can continue to learn by giving it new images, which are abundant on the internet," Hsiao said.
Like all AI systems, bias can creep in through the data sets for Fashion++.
The researchers pointed out that vintage looks are harder to recognise as stylish because training images came from the internet, which has been in wide use only since the 1990s.
Additionally, because the users submitting images were mostly from North America, styles from other parts of the world don't show up as much.
Another challenge is that many images of fashionable clothes appear on models, but bodies come in many sizes and shapes, affecting fashion choices.
The study is scheduled to be presented at International Conference on Computer Vision in Seoul, South Korea.
( With inputs from IANS )