Age Estimation Using Clustering and Semi-Supervised Learning
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Solution Overview
Problem
The existing age estimation apparatus requires a large quantity of image data for accurate age estimation, which necessitates significant manual labeling efforts, making the process labor-intensive.
Innovation Solution
An age estimation apparatus that includes a dimension compression unit, a clustering unit, a labeling unit, and an identification unit, which applies dimension compression, clustering, and labeling to reduce the need for manual labeling by using representative data from clusters for learning, and incorporates unlabeled data in the estimation process through semi-supervised learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large quantity of image data is collected for accurate age estimation, then the accuracy of age estimation is improved, but the labor required for manual labeling increases
Solution Approach 1:
The patent segments the large image dataset into multiple clusters based on feature similarity. By dividing the data into manageable clusters and selecting only representative samples from each cluster for labeling, the system maintains estimation accuracy while significantly reducing the total number of images that require manual annotation.
Solution Approach 2:
The system performs self-labeling through automated clustering and representative sample selection. The clustering unit automatically groups similar images, and the representative data selection unit identifies key samples without human intervention, enabling the system to prepare training data autonomously and reduce reliance on manual labeling.
2Reliability
If manual labeling is performed on all image data, then the quality of training data is improved, but the operation cost increases
Solution Approach 1:
The patent extracts only the essential representative samples from each cluster for manual labeling, rather than labeling all images. This extraction approach ensures that the most informative samples are annotated while discarding redundant data, thereby maintaining training quality with reduced labeling effort and lower operational costs.
Solution Approach 2:
Instead of performing complete labeling on all datasets, the system applies partial labeling only to representative samples that capture the essential variations in the data. This partial action approach provides sufficient training signal for accurate age estimation while significantly reducing the operational burden and costs.
3Loss of information
If feature extraction is performed on high dimensional image data, then the age information is emphasized, but the computational complexity increases
Solution Approach 1:
The patent transforms high-dimensional image data into lower-dimensional feature space through dimensionality reduction techniques. This transformation preserves the essential age-related information while reducing the number of features, thereby decreasing computational complexity and making the clustering and classification processes more efficient.
Data Source
AI summary
Provided are an age estimation apparatus, an age estimation method, and an age estimation program capable of reducing the labor of labeling the image data used for age estimation. An age estimation apparatus for estimating an age of a person on image data includes a dimension compression unit for applying dimension compression to the image data to output low dimensional data; a clustering unit for performing clustering of the low dimensional data outputted; a labeling unit for labeling representative data of each cluster among the low dimensional data clustered; and an identification unit for estimating an age of a person on the basis of a learning result using a feature amount contained in labeled low dimensional data and unlabeled low dimensional data.