Dividing the image into different regions of clothing can create or improve recommendation systems in e-commerce.

Semantic Segmentation of Fashion Images

E - commerce
Dividing an image into semantically independent segments to determine object boundaries
The use of machine learning in online commerce
Gathering data for subsequent annotation
Data Labeling
Highlighting objects in photos to train systems to recognize and interpret them
Data collection
Case Description
2000+ images of people in clothing
20+ markup classes: different types of clothing (pants, skirts, jackets, dresses, t-shirts, shoes, sneakers, headwear, and others) were highlighted on each image
Photoshop segmentation to provide 98% quality markup
Categorization and subcategorization annotation based on specified attributes and classes
Bounding box detection
Provision of data in PNG, JPEG, MATLAB formats and metadata in .csv format
Semantic or instance segmentation
Collection of photos and videos of people in clothing on crowdsourcing platforms
Web scraping and parsing of images of fashionable clothing and lingerie
Business Solutions
Successful fashion forecasting
Dress code control at events
Audit of product lines of competitors
Development and improvement of recommendation systems
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