Styling an outfit is an intricate process that involves theme selection, selection of the primary color, matching of clothing pieces, selection of accessories and getting the right fit.
It is often said that you are what you wear, yet not many people are skilled in making the right decision when it comes to the outfit choice. AI, on the other hand, is capable of driving a clothes swap app to precise decision-making.
From fuzzy logic to genetic algorithms, decision trees to Bayesian networks and neural networks, artificial intelligence software is conveniently positioned to make the work of fashion styling on a day to day basis quick and easy. Each of these AI methods has gained popularity due to their unique abilities gained from years of artificial intelligence development.
Benefits of artificial intelligence methods that can be used in fashion apps
The previous section has enumerated some AI methods that have been used by companies using artificial intelligence. Here are the unique benefits to be obtained from using each of these methods in a fashion app:
- Bayesian networks – they use probabilities to represent variables. They have the ability to infer existing relationships between current and future trends in fashion.
- Fuzzy logic – makes use of approximate reasoning and uncertainty. It is the closest to a human brain regarding being able to interpret truthfulness and falsehood, indicating clear likes and dislikes of the user.
- Artificial neural networks – they are capable of modeling complex styling tasks by modeling preferred outcomes.
- Decision trees – logically allow decision-making.
- Genetic algorithms – assign fitness values that enable the user find exact solutions to an optimization problem.
- Knowledge-based systems – reason out the existence of a relationship between features of style in fashion.
Using artificial intelligence in clothes matching applications
A computer program which intends to use Artificial Intelligence to style its users would have to focus on three main areas:
- Visual garment representation
- Computational imitation of stylist behavior
- The detection and forecasting of fashion trends
Under visual garment representation, the clothes matching app should be able to extract the images of desired outfits. Garments can be described by their unique features including shape, print, color, and fabric. The app would require computer vision techniques to recognize color, shape, and print.
Computer vision techniques automatically recognize color in a Red, Green and Blue model which it converts into a Hue, Saturation, and Intense model. The same goes for shape as these techniques can extract the outline of the garment. In addition to this, the print is detected under the loudness of the garment (the frequency in color changes and locality).
The fabric would require a more specialized AI method as even human beings struggle to identify all fabrics online by sight alone. Stylistic semantic correlations would come in handy. They would entail having a system that relates certain attributes to certain fabrics to make a prediction. For example, casual T-shirts would be related to cotton fabric, formal dinner dress - to silk and so on.
Next, this clothes matcher would require the ability to model human stylist behavior. Once the garment has been located, there would be a need for computational styling.
The first aspect of styling is color harmonization. An ideal app would be one which can take a standard color scheme and adapt it to the user’s preferences. Interactive artificial intelligence algorithms programming would be able to adapt such color scheme in real time by using schemes that have additional labels such as “slightly,” “neutral,” “extremely” instead of plain colors.
The second aspect of styling would include the styling of shape, prints, and fabrics. Many factors go into personal preference of shape, print, and fabrics including the current fashion trends, the occasion and cultural background of the user. The ideal apps that help you choose your outfit would use a neural network model that converts the physical attributes of a garment into a sensation. For example, color into temperature, shape into fit, fabric into softness and so on.
Thus, as a user, keying in words “garment with a soft feel on a summer evening” into the application will automatically let artificial intelligence guess your preferred garment fabric and color.
Finally, this app should be able to track the fashion trends. An ideal outfit picker would be extremely sensitive to past, current and future fashion trends. This would require a combination of Bayesian networks and knowledge-based systems.
The Bayesian network would be used to model a human stylist’s trend proposals. Based on knowledge of the past and current trend, the Bayesian network would be able to classify trends into binary target values which will then be proposed (or not) based on the probability of their reoccurrence.
In summary, the following applications of artificial intelligence would drive this app:
- Use computer vision techniques to extract the desired image of the outfit
- Use interactive genetic algorithms to match colors of different pieces
- Use neural networking to select the desired shape print and fabric
- Use Bayesian networks to select items based on future fashion trends
The pros of artificial intelligence technology
Many advantages assure the future of artificial intelligence applications in fashion and other areas:
- It deals with tasks that humans would find boring to do on a daily basis.
- It quickens the decision-making process, where one would take hours to decide on an outfit for the day.
- It does away with the margin of error. When the right information is fed into the app to try on clothes, the outcome is accurate.
- It takes the stress away from an individual. Using an app to make outfits with your own clothes would reduce by a considerable amount the stress that comes with choosing outfits daily.
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