Fine-Grained Object Classification via Camera Pose Alignment
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Solution Overview
Problem
Fine-grained object classification is challenging due to the high degree of shape similarity among objects in a category, making it difficult to label and localize discriminative parts and learn appearance descriptors effectively.
Innovation Solution
A system and method that estimate the camera pose of an input image, align it using a feature database, and classify the object through unsupervised feature extraction with Convolutional Sparse Coding (CSC) and manifold learning, reducing model complexity and computation time by using a single-layer feature extraction scheme.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual parts annotation or image segmentation is used for labeling/localizing discriminative parts, then classification accuracy can be improved, but the complexity and time consumption increase significantly
Solution Approach 1:
The patent performs camera pose estimation and image alignment as preliminary actions before classification. By aligning the input image to a canonical view using pose estimation, the system prepares the data in advance, making subsequent classification more accurate without requiring complex manual annotation for each viewing angle.
Solution Approach 2:
The system changes the parameter of image orientation by estimating camera pose and aligning images to a canonical view. This transformation of spatial parameters allows the classification algorithm to work with standardized inputs, improving accuracy while reducing the need for manual annotation of parts in various orientations.
2Measurement precision
If multi-layer deep neural network is used for learning appearance descriptors, then feature extraction capability is improved, but computation time and model complexity increase
Solution Approach 1:
The patent segments the feature extraction process into two parts: (1) using pose estimation and alignment to normalize images, and (2) applying a simpler single-layer descriptor extraction. This segmentation allows the system to achieve effective feature extraction without requiring deep multi-layer networks, thus reducing computation time.
Solution Approach 2:
Instead of applying excessive computational resources through multi-layer deep networks, the system uses partial action by combining pose-based alignment with a single-layer descriptor method. This partial approach achieves sufficient feature extraction capability for fine-grained classification while avoiding the excessive computation time of deep networks.
3Measurement precision
If pose estimation and image alignment are performed, then classification accuracy for fine-grained objects is improved, but computational cost increases
Solution Approach 1:
The system performs pose estimation and alignment as preliminary actions that enable more accurate classification. By preparing images in advance with correct orientation and positioning, the subsequent classification requires less computational effort to achieve high accuracy, balancing the overall computational cost.
Data Source
AI summary
One embodiment provides a method comprising estimating a camera pose of an input image and aligning the input image to a desired camera pose based on a feature database. The input image comprises an image of a fine-grained object. The method further comprises classifying the object based on the alignment.