ISP Classifier Evaluation via Image Subset Segmentation
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Solution Overview
Problem
The existing methods for training and evaluating AI classifiers for object recognition, such as face detection, are computationally intensive and time-consuming, requiring numerous rounds of classification and large training image sets, which increases costs and processing time.
Innovation Solution
A system and method for evaluating a classifier within an image signal processor (ISP) that selects subsets of images based on divider positions, classifies them, determines positive-match and error counts, and identifies optimal divider positions for minimum or maximum error counts to assess the classifier's optimality, thereby reducing the number of operations needed for evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a classifier is evaluated by classifying all images in a large training image set, then the evaluation accuracy is improved, but the computational time and cost increase significantly
Solution Approach 1:
The training image set is divided into multiple subsets based on divider positions. Instead of evaluating all images, the system selects specific subsets at different divider positions (e.g., 10%, 30%, 50%, 70%, 90% divisions) for classification. This segmentation approach maintains evaluation accuracy by sampling diverse portions of the dataset while dramatically reducing computational time and resources required.
2Measurement precision
If multiple rounds of training and evaluation are performed to improve classifier accuracy, then the classification performance is improved, but the development cost and time increase
Solution Approach 1:
The system performs preliminary selection of image subsets at various divider positions before the actual classification evaluation. By pre-identifying which subsets to evaluate based on divider positions, the system prepares the evaluation framework in advance, enabling faster execution of multiple training rounds without proportionally increasing development time and cost.
3Productivity
If a subset of images is selected for evaluation instead of the full training set, then the computational burden is reduced, but the evaluation accuracy may deteriorate
Solution Approach 1:
The system introduces a new dimension for evaluation by considering multiple divider positions across the training image set. Instead of evaluating either the full set or a single random subset, it evaluates multiple subsets at different positional divisions (10%, 30%, 50%, 70%, 90%), thereby maintaining comprehensive coverage and accuracy while reducing the number of images classified in each individual evaluation round.
Data Source
AI summary
A system for evaluating a classifier of an image signal processor (ISP) includes (i) a microprocessor and (ii) memory storing training images, the microprocessor being capable of sending each training image to the ISP. The system includes machine-readable instructions stored within the memory and executed by the microprocessor capable of: (i) selecting a subset of images based upon a divider position, (ii) controlling the ISP to classify each image as belonging or not belonging to an object class, (iii) determining a positive-match count, (iv) determining an error count based upon the positive-match count and total number of training images belonging to the object class, (v) repeating, for other divider positions, steps of selecting, controlling, and determining to identify an optimal divider position and a minimum-error count; and (vi) determining the classifier's optimality by comparing the optimal divider position to a predetermined optimal divider position and a predetermined minimum-error count.