Bayesian Model Combination for Text and Image Classification
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Solution Overview
Problem
Existing systems face challenges in combining and associating electronic image data and text-based data, as they are processed using different computer systems and cannot be easily linked, making it difficult to effectively combine distinct classification models for categorization.
Innovation Solution
The method involves using a processor to execute a first random forest classifier on attributes and a second random forest classifier on images, combining the results into a Bayesian Model Combination (BMC) to classify items effectively, leveraging both text and image data for accurate categorization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image data and text data are processed separately using different classification models, then each data type can be processed with its specialized model, but the models cannot be effectively combined to improve overall classification accuracy
Solution Approach 1:
The patent introduces an intermediary mechanism (Bayesian Model Combination) that mediates between the image classification model and text classification model. This intermediary layer harmonizes the outputs from both specialized models, allowing them to be effectively combined without direct complex interactions, thus improving overall classification accuracy while managing complexity through a structured combination framework
2Reliability
If multiple classification models are combined, then classification accuracy can be improved, but the system complexity and difficulty of integration increase
Solution Approach 1:
The patent merges multiple classification models (image-based and text-based) into a unified classification system. By combining the specialized models rather than keeping them separate, the system achieves improved reliability and categorization accuracy while the merging process itself manages the integration complexity through a cohesive framework
3Ease of manufacture
If different data types are processed independently, then processing can be simplified, but the data cannot be associated or linked effectively
Solution Approach 1:
The patent creates a universal classification framework that can process multiple data types (image and text) through a common Bayesian Model Combination mechanism. This multi-functional system maintains processing simplicity by using a unified approach while preventing information loss by effectively associating and linking different data types through the combination framework
Data Source
AI summary
Systems, methods, and computer-readable storage media for categorizing items based on attributes of the item and a shape of the item, where the shape of the item is determined from an image of the item. An exemplary system configured as disclosed herein can receive a request to categorize an item, the item having a plurality of attributes, and receive an image of the item. The system can identify, via a processor configured to perform image processing, a shape of the item based on the image, and transform the plurality of attributes and the shape of the item, into a plurality of quantifiable values. The system can then categorize the item based on the quantifiable values.