Knit Fabric Image Search Using Construction Attribute Extraction
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Solution Overview
Problem
Conventional methods of organizing fabric data do not allow for easy searching, categorizing, or matching of fabrics with similar construction characteristics, leading to inefficiencies in fabric design and production due to unnecessary trial and error or excessive collaboration.
Innovation Solution
A digital image-based system utilizing AI and machine learning to analyze and categorize knit fabrics based on detailed construction attributes, enabling a user interface for efficient searching and reproduction of fabric designs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If physical fabric samples are stored in sample books or libraries and physically circulated, then designers can review and discuss fabric designs, but collaboration becomes difficult for designers at separate or remote locations and time is lost in physically locating and circulating samples
Solution Approach 1:
The patent creates digital copies of physical fabric samples through high-resolution photography and stores them in an accessible database. These digital replicas include detailed images from multiple angles, construction attribute data, and visual cues that allow designers to review and discuss fabric designs remotely without physically handling or transporting samples, thereby enabling seamless collaboration across locations and eliminating time loss associated with sample circulation
Solution Approach 2:
The patent transitions the fabric sample system from physical three-dimensional space to digital information space. By converting physical samples into digital images and structured data with visual cues and construction attributes, the system allows unlimited access from any location simultaneously, removing the constraints of physical sample book storage and enabling remote collaboration without time delays
2Ease of manufacture
If basic fabric information such as color, generic construction tags, fabric type and elements are provided, then simple fabric identification is possible, but the information lacks the specificity needed for future production or reproduction of the fabric
Solution Approach 1:
The patent segments fabric information into multiple hierarchical levels: basic information (color, fabric type), construction attributes (stitch type, yarn composition, gauge), and visual cues (pattern characteristics, texture descriptors). This segmentation allows the system to provide detailed, specific construction information necessary for accurate fabric reproduction while organizing data in a structured manner that maintains ease of manufacture by enabling targeted retrieval of specific construction parameters
Solution Approach 2:
The patent performs preliminary analysis of fabric samples to extract and store comprehensive construction attributes and visual cues before production or reproduction is needed. By pre-processing samples to identify and catalog detailed construction information including stitch patterns, yarn characteristics, and visual features, the system ensures that all necessary specific information is readily available when reproduction is required, eliminating the need for additional analysis later
3Productivity
If conventional methods of organizing fabric data are used, then simple storage is achieved, but easy searching, organizing or matching of fabrics with similar construction characteristics is not allowed, leading to inefficiencies and unnecessary trial and error
Solution Approach 1:
The patent implements feedback mechanisms where the system analyzes fabric samples to automatically extract construction attributes and visual cues, then uses this extracted information to improve future searches and matching. The system learns from previous search patterns and matching results to refine its organization and retrieval methods, enabling increasingly efficient fabric searches and reducing trial and error by providing more accurate matching based on construction characteristics
Solution Approach 2:
The patent transforms fabric organization from basic categorical parameters (fabric type, color) to multiple detailed parameters including construction attributes (stitch type, gauge, yarn composition) and visual cues (pattern characteristics, texture). This parameter expansion enables sophisticated searching and matching capabilities that can quickly identify fabrics with similar construction characteristics, dramatically improving productivity and eliminating time loss from trial and error by providing precise parameter-based filtering and comparison
Data Source
AI summary
Systems and methods are discussed herein and include a method of providing for search and review of an electronic database of knit fabric samples, the method including: providing a plurality of digital images of a plurality of knit fabrics; organizing the plurality of digital images in the database based upon corresponding visual cues extracted using an image analysis performed on each of the plurality of digital images, wherein the visual cues are construction attributes of the plurality of knit fabrics; displaying one or more of the plurality of digital images for review; and providing for search of the database using one or more of the visual cues.