Feature Extraction
What Is Feature Extraction?
Feature extraction is the process of reducing raw data into a set of meaningful attributes or features that can be used for analysis or machine learning tasks. In visual search, for example, it involves identifying unique patterns, shapes, textures, or colors within an image to enable precise object recognition.
Analyze Your Use Case
NYRIS uses feature extraction to analyze images of products or spare parts, isolating key characteristics to enable instant identification across 500 million items.
How Does Feature Extraction Work?
- Data Collection: Raw data, such as images or sensor readings, is gathered from cameras, CAD models, or IoT devices. For example, NYRIS collects high-resolution product images for training its visual search engine.
- Preprocessing: The data is cleaned and normalized to remove noise or irrelevant information. NYRIS applies advanced preprocessing techniques like contrast adjustment and edge detection to enhance image quality before analysis.
- Feature Isolation: Algorithms like Convolutional Neural Networks (CNNs) identify critical features such as edges, textures, or shapes that distinguish one object from another. NYRIS’s proprietary algorithms extract thousands of features per image with 99.7% accuracy, even in complex environments like warehouses or factory floors.
- Dimensionality Reduction: To optimize performance, redundant features are removed while retaining the most important ones. This step ensures faster processing without compromising accuracy.
Industrial Applications
Manufacturing
- Spare Parts Identification: Feature extraction enables technicians to identify replacement parts instantly by analyzing serial numbers or structural details from machinery images. DMG Mori reduced downtime by 72% using NYRIS’s solution.
E-commerce
- Product Discovery: Retailers like IKEA use feature extraction to tag product attributes (e.g., size, color) for personalized recommendations and search optimization, increasing conversion rates by 35%.
Retail Inventory Management
- Shelf Scanning: METRO employs feature extraction to track stock levels by analyzing product labels and packaging designs on shelves with 98% accuracy, reducing manual audits by 85%.
Benefits For Your Company
- Enhanced Accuracy: Achieve near-perfect precision (99.7%) in object recognition tasks by isolating the most relevant features from raw data.
- Faster Processing Times: Reduce analysis time with optimized feature sets, enabling sub-second responses for applications like visual search or defect detection.
- Improved Scalability: Handle large datasets efficiently by focusing only on critical attributes, making it ideal for industries managing millions of SKUs or components.
FAQs
How does feature extraction improve AI performance?
By isolating only the most relevant attributes from raw data, feature extraction reduces computational complexity while improving model accuracy and efficiency.
Can feature extraction work with low-quality images?
Yes! NYRIS employs preprocessing techniques like noise reduction and contrast enhancement to optimize low-quality images for feature analysis.
What industries benefit most from feature extraction?
Industries such as manufacturing, retail, e-commerce, and automotive benefit significantly by automating processes like quality control, inventory management, and product discovery.
About NYRIS
Founded in 2015 in Berlin, NYRIS is a leader in AI-powered visual search and synthetic data solutions tailored for industries like manufacturing and retail. With €10 million in funding from investors such as Trumpf Venture and IKEA, NYRIS processes over 500 million products with sub-second speeds using advanced feature extraction technology.