Unsupervised Object Recognition via Dimensionality Reduction
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Solution Overview
Problem
Conventional digital video systems face challenges in automatically recognizing real-world objects within large datasets, as they rely on expensive and time-consuming human-labeled training sets, and unsupervised learning techniques have not been effectively achieved for large-scale image recognition systems.
Innovation Solution
An object recognition system performs multiple rounds of dimensionality reduction and consistency learning on visual content items to generate feature vectors that accurately represent objects, allowing for unsupervised learning and efficient recognition without human input, using clustering algorithms to determine similarity and store feature vectors associated with object names.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised learning with manually labeled training sets is used, then object recognition accuracy is improved, but time consumption and cost increase significantly
Solution Approach 1:
The system performs self-service by automatically generating training data through unsupervised learning. It segments visual content items, extracts features, performs dimensionality reduction, and identifies object representations without requiring manual human labeling, thereby eliminating the time-consuming and expensive supervised learning process while maintaining recognition accuracy
Solution Approach 2:
The patent replaces the mechanical human labeling process with an automated computational system. Instead of relying on manual examination and labeling by humans, the system uses automated segmentation, feature extraction, dimensionality reduction, and clustering algorithms to identify and label objects, substituting human mechanical effort with computational processes
2Measurement precision
If supervised learning with manually labeled training sets is used, then object recognition accuracy is improved, but scalability to large datasets deteriorates
Solution Approach 1:
The system achieves scalability through self-service automation. It processes large datasets of visual content items automatically by segmenting them, extracting features, performing dimensionality reduction, and identifying object representations without human intervention, enabling the system to scale to handle hundreds of thousands of objects and millions of images that would be impossible to label manually
Solution Approach 2:
The patent replaces the non-scalable human labeling mechanism with an automated computational system that can process vast amounts of data. The system uses algorithms for segmentation, feature extraction, dimensionality reduction, and clustering that can be applied consistently across large datasets, providing scalability that human operators cannot match
3Measurement precision
If dimensionality reduction rounds are increased to improve feature accuracy, then object representation quality is improved, but computational complexity increases
Solution Approach 1:
The system applies partial action by performing a limited number of dimensionality reduction rounds (e.g., 1-3 rounds) rather than exhaustive reductions. This partial application of dimensionality reduction achieves sufficient feature accuracy for object recognition while avoiding the excessive computational complexity that would result from multiple extensive reduction rounds
Solution Approach 2:
The system changes parameters by adjusting the number of dimensionality reduction rounds based on the specific requirements of different object names. The patent allows the number of rounds to vary with respect to different object names, optimizing the balance between feature accuracy and computational complexity for each case rather than applying a fixed high number of rounds to all objects
Data Source
AI summary
An object recognition system performs a number of rounds of dimensionality reduction and consistency learning on visual content items such as videos and still images, resulting in a set of feature vectors that accurately predict the presence of a visual object represented by a given object name within an visual content item. The feature vectors are stored in association with the object name which they represent and with an indication of the number of rounds of dimensionality reduction and consistency learning that produced them. The feature vectors and the indication can be used for various purposes, such as quickly determining a visual content item containing a visual representation of a given object name.