Image Classification for Trend Forecasting Using Visual Tokens
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Solution Overview
Problem
Current image search technologies in e-commerce are inefficient for large collections, leading to poor search results and difficulties in inventory management, as they rely on metadata and cannot accurately classify images to forecast consumer trends or handle out-of-stock situations effectively.
Innovation Solution
A method and system for image classification and trend forecasting using visual tokens generated from image data sets, which involve detecting objects, determining feature descriptors, and classifying objects to identify trendsetters and mainstream adoption of attributes, enabling accurate forecasting of trends and inventory management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current image search technologies based on metadata and histograms are used, then the system is simple to operate, but the search accuracy and image classification precision deteriorate for large collections
Solution Approach 1:
The patent replaces traditional metadata-based and histogram-based image search mechanisms with deep learning-based visual feature extraction and embedding systems. Convolutional neural networks automatically learn hierarchical visual features from images, substituting manual feature engineering and simple statistical methods with intelligent, adaptive feature representation that achieves high accuracy on large-scale image collections.
Solution Approach 2:
The patent transforms image representation from fixed-dimensional histograms and metadata to dynamic, learned visual embeddings in a high-dimensional space. By changing the parameter space from hand-crafted features to learned features, the system achieves both high classification accuracy and efficient similarity search through operations like cosine similarity on embedding vectors.
2Measurement precision
If visual token generation and classification is implemented, then trend forecasting accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent performs visual feature extraction and embedding generation as preliminary actions during image ingestion and indexing, before trend analysis is needed. By pre-processing images to extract visual tokens and compute embeddings in advance, the system avoids repeated heavy computation during trend forecasting, significantly reducing query-time processing delays while maintaining high accuracy.
Solution Approach 2:
The patent creates compact visual token representations and embedding vectors as simplified copies of full-resolution images. These compressed visual features capture essential stylistic and attribute information needed for trend forecasting, enabling accurate analysis without processing the complete high-resolution image data repeatedly.
3Reliability
If image-based product recommendations are provided, then customer satisfaction and sales improvement increase, but search result relevance deteriorates with current technology
Solution Approach 1:
The patent segments images into distinct visual components and attributes (e.g., color, pattern, style, shape) through hierarchical feature extraction. By dividing the image analysis into meaningful segments, the system can perform precise similarity matching on specific product attributes, generating relevant recommendations based on multiple visual dimensions rather than overall image similarity alone.
Solution Approach 2:
The patent applies different analysis methods and feature extraction techniques to different regions and attributes of images. By focusing computational resources on locally important features (e.g., emphasizing color matching for fashion items, or pattern recognition for textiles), the system achieves higher recommendation relevance tailored to specific product categories and customer preferences.
Data Source
AI summary
Systems and methods for forecasting trends using image data is disclosed. The method comprises determining trendsetter visual tokens of interest associated with an image data set of one or more trendsetters for a particular object category; determining a trendsetter adoption of one or more particular attributes of an object for the particular object category from the trendsetter visual tokens of interest; determining mainstream visual tokens of interest associated with an image data set of one or more mainstream adopters for the particular object category; determining a mainstream adoption of the one or more particular attributes of the object for the particular object category from the mainstream visual tokens of interest; and determining a state of a trend for the one or more particular attributes of the object based on the trendsetter adoption and the mainstream adoption.