Produce Recognition Using Hierarchical Bayesian Kernel Combination
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Solution Overview
Problem
Current methods for automatically identifying fruits and vegetables at checkout lanes are inefficient and inaccurate, leading to revenue loss and potential fraud, as they fail to effectively combine diverse features and signals for robust classification.
Innovation Solution
A produce recognition method utilizing hierarchical Bayesian learning and kernel combination for synergistic data integration from multiple sources, including images, aromas, and other sensors, to create a composite similarity description for accurate classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple recognition methodologies are combined to improve accuracy, then recognition accuracy is improved, but classifier design complexity increases
Solution Approach 1:
The patent segments the classification task into multiple independent kernel functions, each handling a specific feature type (spectral, image, aroma). Each kernel operates independently on its feature input and produces a separate similarity measure, which are then combined through weighted summation. This segmentation allows complex multi-modal classification to be broken down into manageable, independent components that can be designed and tuned separately.
Solution Approach 2:
The patent creates a universal classifier framework that can handle multiple types of features (spectral, image, aroma, and potentially future sensor types) through a single unified decision function. The kernel combination approach provides a multi-functional classification system that can accommodate diverse feature modalities without requiring separate classification algorithms for each type, thus reducing overall system complexity while maintaining high accuracy.
2Ease of operation
If traditional classifiers treat all features equally and independently, then classifier simplicity is maintained, but recognition accuracy deteriorates due to ignoring feature correlations and importance differences
Solution Approach 1:
The patent applies local quality by assigning different weights to different features and feature types based on their individual importance and reliability. The weighting function allows each kernel (spectral, image, aroma) to contribute differently to the final classification decision, with more reliable or discriminative features receiving higher weights. This enables the system to emphasize critical features while downweighting less important ones, improving accuracy without significantly complicating the classifier structure.
Solution Approach 2:
The patent changes the parameters of the classification system by introducing learnable weight parameters for each feature type and kernel bandwidth parameters that control the similarity measurement sensitivity. These parameters are optimized during training to maximize recognition accuracy. The ability to adjust these parameters allows the classifier to adapt to different produce types and conditions, significantly improving performance over fixed, equal-weight approaches.
Data Source
AI summary
A produce recognition method which uses hierarchical Bayesian learning and kernel combination, and which offers classification-oriented synergistic data integration from diverse sources. An example method includes providing a classifier having a plurality of inputs, each input being adapted to receive produce data of a different modality; mapping the produce data to the respective input of the classifier by a computer; for each input, independently operating on the data relating to that input to create a feature set by the computer; comparing each feature in the feature set to respective pre-trained data for that feature to produce a similarity description set; combining all similarity description sets using a dedicated weighting function to produce a composite similarity description by the computer; and deriving a plurality of class values from the composite similarity description to create a recognition result for the produce item by the computer.