Dynamic Classifier Selection for Image Stream Processing
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Solution Overview
Problem
Image analysis for object identification, such as face recognition, is computationally expensive and slow, leading to user frustration and inefficiency in computing devices.
Innovation Solution
A dynamic classifier selection system that uses a general classifier and specialized classifiers based on class skew, switching between them dynamically to optimize resource usage and speed, by training specialized classifiers for specific subsets of objects frequently encountered in an image stream.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a general classifier is used to classify multiple aspects of content in an image stream, then the system can handle diverse objects, but the computational cost and processing time increase
Solution Approach 1:
The patent divides the classification task into multiple specialized classifiers, each handling a specific subset of objects (e.g., faces, vehicles, animals). This segmentation allows the system to process different object types in parallel using optimized classifiers, improving overall processing speed while maintaining comprehensive classification coverage.
Solution Approach 2:
The system dynamically selects which specialized classifier to use based on the content of each image. The classifier selection module analyzes the image stream and chooses the appropriate specialized classifier for the current frame, enabling adaptive optimization of processing speed without sacrificing classification versatility.
2Productivity
If a specialized classifier is trained for a specific subset of objects, then processing speed improves for that subset, but the system requires multiple classifiers and increased complexity
Solution Approach 1:
The patent introduces a classifier selection module as an intermediary that manages the coordination between multiple specialized classifiers. This module receives image input, determines which specialized classifier is appropriate for the current image content, and routes the image to the selected classifier, thereby simplifying the management complexity of having multiple specialized classifiers.
Solution Approach 2:
The system design allows specialized classifiers to be trained on common feature models, creating a hierarchy where specialized classifiers inherit general object recognition capabilities while adding specific object specialization. This multi-functionality reduces the need for completely separate classifier systems.
3Reliability
If image analysis is performed using comprehensive classification, then accuracy is maintained, but computational resources and power consumption increase
Solution Approach 1:
By segmenting the classification task into specialized classifiers for different object subsets, the system can apply computationally efficient classification methods tailored to each object type. This segmentation maintains classification accuracy for each specific object category while reducing overall computational resource requirements compared to a single comprehensive classifier.
Solution Approach 2:
The system changes the parameter of classifier specialization by training different classifiers with different parameter sets optimized for specific object types. This allows each classifier to operate with parameters tuned for its specific domain, achieving high accuracy with lower computational overhead than a general-purpose classifier.
Data Source
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AI summary
A classification system classifies different aspects of content of an input image stream, such as faces, landmarks, events, and so forth. The classification system includes a general classifier and at least one specialized classifier template. The general classifier is trained to classify a large number of different aspects of content, and a specialized classifier can be trained based on a specialized classifier template during operation of the classification system to classify a particular subset of the multiple different aspects of content. The classification system determines when to use the general classifier and when to use a specialized classifier based on class skew, which refers to the temporal locality of a subset of aspects of content in the image stream.