Neural Network Object Recognition System with Segmented Attribute Layers
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Solution Overview
Problem
Current object recognition methods using artificial neural networks face challenges in accurately determining object attributes and age information, particularly in distinguishing between attributes like gender and race, and estimating age based on these classifications.
Innovation Solution
The proposed method involves an object recognition system that extracts feature data using an object model, determines attribute classification and age information through dedicated layers, and estimates object age by calculating probabilities for attributes and ages, utilizing a neural network structure with multiple layers to classify and train on input data, including back-propagation for weight updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single neural network is used for object recognition, then the system is simple, but the accuracy of attribute classification and age estimation is insufficient
Solution Approach 1:
The patent divides the object recognition system into separate functional modules: an object recognition model for basic identification, a classification layer for attribute classification (gender, race), and an attribute layer for age estimation. Each layer processes specific aspects independently, improving accuracy for each attribute while maintaining manageable system complexity through modular design.
Solution Approach 2:
The patent extends the traditional single-network approach by adding dimensional layers (classification layer and attribute layer) that process different aspects of object attributes simultaneously. This multi-dimensional approach allows the system to extract and classify multiple attributes from the same feature representations without requiring completely separate systems.
2Measurement precision
If multiple layers and classification approaches are used, then attribute classification accuracy improves, but computational complexity increases
Solution Approach 1:
The computational task is segmented across different layers: the object recognition model extracts features, the classification layer handles attribute classification, and the attribute layer performs age estimation. This segmentation allows each component to specialize in specific computations, improving accuracy while distributing computational complexity across modular units that can be optimized independently.
Solution Approach 2:
The feature representations extracted by the object recognition model serve multiple purposes: they are used by both the classification layer for attribute classification and the attribute layer for age estimation. This multi-functionality allows the system to reuse computational results across different tasks, reducing redundant computations despite having multiple layers.
3Reliability
If probability-based classification is used for multiple attributes, then classification robustness improves, but processing time increases
Solution Approach 1:
The object recognition model performs preliminary feature extraction that serves as the foundation for both classification and age estimation tasks. By pre-processing and extracting comprehensive features first, the system avoids redundant processing later when performing probability-based classifications, thus reducing overall processing time while maintaining robust classification through probability calculations in subsequent layers.
Data Source
AI summary
Provided is a method and apparatus to recognizing an object based on an attribute of the object and training that may calculate object age information from input data using an attribute layer trained with respect to an attribute of an object and a classification layer trained with respect to a classification of the object. The method to recognize the object includes extracting feature data from input data including an object using an object model, determining attribute classification information related to the input data from the feature data using a classification layer, determining attribute age information related to an attribute from the feature data using an attribute layer, and estimating object age information based on the attribute classification information and the attribute age information.