Hybrid Deep Learning Architecture for Image Object Recognition
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Solution Overview
Problem
Current object recognition models in images face challenges in balancing feature invariance and diversity, particularly struggling with objects of irregular shapes and varied textures, and are not scalable for high-dimensional data.
Innovation Solution
A deep model-based and data-driven hybrid architecture that combines a deep learning pipeline for learning image features progressively with a data-driven pipeline for extracting features from unlabeled images, using sparse regularization and supervised learning to enhance object prediction, and employs edge selection, pooling, and part pooling to create representative patches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a model-based approach is used for feature extraction, then feature invariance is improved, but feature diversity deteriorates
Solution Approach 1:
The patent merges model-based approach (for invariance) and data-driven approach (for diversity) into a hybrid architecture. The deep learning pipeline extracts model-based features while the data-driven pipeline processes unlabeled images to capture diverse patterns. Sparse regularization then combines these two feature sets, allowing the system to achieve both invariance and diversity simultaneously.
Solution Approach 2:
The patent creates a composite feature representation by combining model-based features (from deep learning pipeline) and data-driven features (from heuristic-based pipeline). This composite approach allows the system to leverage the strengths of both methodologies: the invariance properties of model-based features and the diversity capture capabilities of data-driven features.
2Adaptability or versatility
If a data-driven approach is used for feature extraction, then feature diversity is improved, but feature invariance deteriorates
Solution Approach 1:
The patent merges model-based approach (for invariance) and data-driven approach (for diversity) into a hybrid architecture. The deep learning pipeline extracts model-based features while the data-driven pipeline processes unlabeled images to capture diverse patterns. Sparse regularization then combines these two feature sets, allowing the system to achieve both invariance and diversity simultaneously.
Solution Approach 2:
The patent creates a composite feature representation by combining model-based features (from deep learning pipeline) and data-driven features (from heuristic-based pipeline). This composite approach allows the system to leverage the strengths of both methodologies: the invariance properties of model-based features and the diversity capture capabilities of data-driven features.
3Measurement precision
If traditional object recognition models are used, then accuracy is improved, but scalability to high-dimensional data deteriorates
Solution Approach 1:
The patent segments the feature extraction process into two independent pipelines: a deep learning pipeline for model-based feature extraction and a data-driven pipeline for heuristic-based feature extraction. This segmentation allows each pipeline to be optimized independently and enables the system to handle high-dimensional data by processing it through multiple parallel pathways before combining results via sparse regularization.
Solution Approach 2:
The patent introduces a new dimension to the feature extraction process by adding the sparse regularization step that operates on the combined feature spaces. This additional processing dimension enables the system to manage high-dimensional data by transforming and regularizing features across multiple pipelines before final classification.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for labeling images. In one aspect, a method includes automatically identifying an object in an image using a deep model-based and data-driven hybrid architecture.