Monday, December 11, 2023

When fashion meets AI

Deepak Halan is an associate professor at School of Management Sciences, Apeejay Stya University

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2. Virtual fitting systems to fill one of the major gaps between e-commerce and retail stores. It enables customers to go through the product line, watch over product reviews and gain more confidence about the item being purchased.

3. Recommender system implementations in e-commerce to recommend new items based on users’ browsing histories.

4. Online fashion communities to provide a platform for people to share, gain and communicate fashion inspiration and shopping information online.

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How AI helps in fashion design

Garment representation

As mentioned earlier, a set of basic features such as colour, shape, print and fabric is used to describe garments. Computer vision techniques automatically recognise the first three features. Colour is represented in RGB (red, green and blue) format, which is later converted into HSI (hue, saturation and intense) model.

Shape is easily extracted using computer vision techniques and print of a garment is considered in its loudness. Loudness is defined by the frequency of colour changes in the garment and also by changes in placement on the garment.

Relationship between RGB and HSI colour models (Source:
Fig. 4: Relationship between RGB and HSI colour models (Source:

Fabric is the most complex aspect as even humans have trouble in recognising a fabric just by looking at it. One possible solution for tricky attributes like fabric is stylistic semantic correlations between clothing attributes. Systems learn correlations between attributes and then intelligently fill in missing values. For example, for a formal dress there is a high possibility of it being made of silk, whereas casual wear such as t-shirts are more likely to be made of cotton.

Schematic overview of Smart Fashion architecture
Fig. 5: Schematic overview of Smart Fashion architecture

Support vector machine classifiers are applied on all attributes to determine how useful these attributes would be in prediction. Then, the system makes predictions based on the inference of different attributes and mutual dependency relations.

Colour harmony evaluation

Matsuda Colour Coordination (MCC) developed by Yutaka Matsuda is used to evaluate the harmony rate between colours. A total of 80 colour schemes (8 hue types and 10 tone types) are used. A colour scheme is considered harmonious if there are many samples in the system.

Each individual has a unique style preference. An intelligent style system can instantaneously adapt from a standard colour scheme to suit a user’s personal preference. Linguistic labels (neutral, a little, slightly, fairly, very and extremely) are added on the colour scheme and presented as fuzzy sets, respectively. The system adapts to a user’s preference during its interaction with the user.

Shape, prints and fabric styling

The next requirement in fashion styling is to identify attributes like shape, print and fabric. Style principles of these attributes change with time and heavily depend on individual preferences. For example, for wearing prints, the style trend used to be only one pattern piece at a time. Later, wearing two patterns with similar shades became trendy. Other features which affect the styling are occasion and cultural background. The stylist needs to look at a number of features from different categories and classify items based on his experience and fashion sense. Updated style rules are a great data source to keep track of changes in fashion trends.

Fashion trend tracking

The stylist program needs to be highly sensitive to fashion trends. Fashion trend forecasting is vital for success in the fashion industry and always poses a bigger challenge than prediction of other fields. The highest variable is the human value.
In the fashion process, new trends start becoming popular slowly before trendsetter adoption and then reach a peak of fast social majority acceptance. These decline dramatically thereafter owing to reasons such as newer trends arrival and boredom setting in the trend after peak.

There are two types of fashion forecasting. One concentrates on current fashion objects and makes predictions on fashion colour trends. The other is based on a longer vision, for example, black and white are never out of date regardless of season or occasion.

Attempts to predict fashion colour trends with AI techniques have seen great success. AI is capable of modelling individual boredom as memory factor of an object and memory parameter as the number of times it is used. Greater frequency of usage means more memories, and thus higher boredom and less utility.

AI methods include:

1. Fuzzy logic. It utilises uncertainty and approximate reasoning and works closer to human brain, providing outputs as straightforward like or dislike.

2. Artificial neural networks (ANNs). This is a learning method, resembling animal nervous systems, mapping input to a target output by adjusting weights. It is suitable for modelling complex styling tasks with multiple features.

3. Decision trees. This method is used in human decision-making models and includes tree-structured graphs that represent attributes as internal nodes and outcomes as branches.

4. Knowledge-based systems. These are programs that represent knowledge and solve complex problems by reasoning out how knowledge artefacts are related or not related. These are used to show relationships between features in fashion styling.


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