Part-Based Object Models for Scale Invariant Classification
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Solution Overview
Problem
Existing object classification systems face challenges in detecting and classifying objects due to variations in appearance, viewpoint, and scale, as well as being computationally expensive and unable to generalize effectively in cluttered backgrounds.
Innovation Solution
The system learns part-based object models using shape features like histograms of gradients (HOG) and appearance features like pyramid histograms of visual words (PHOW), allowing for robust object classification by parsing objects into parts and using these features to classify objects invariant to scale and rotation without requiring viewpoint labels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing object classification systems use codebook-based detection methods, then object recognition can be achieved, but the detected object features are sparse and only generate a sparse set of object objects
Solution Approach 1:
The patent divides objects into multiple parts or components, learning part-based models that capture local features and their configurations. This segmentation approach transforms sparse global object features into dense local part features, enabling comprehensive object representation through part assemblies.
2Adaptability or versatility
If existing object classification systems specify the number of parts during learning or use motion cues from video sequences, then object models can be learned, but the systems cannot generalize effectively and are computationally expensive
Solution Approach 1:
The patent employs dynamic programming to efficiently search and evaluate part configurations, enabling the system to adapt to varying object structures without pre-specifying part numbers. This dynamic approach allows flexible generalization to different objects while maintaining computational efficiency through optimal substructure exploitation.
3Adaptability or versatility
If existing object classification systems process digital images with significant scale and viewpoint changes, then comprehensive object detection can be attempted, but the computational cost increases significantly
Solution Approach 1:
By segmenting objects into parts, the system can process local regions independently and combine results, reducing the computational burden of handling scale and viewpoint variations in entire images. Each part can be detected and matched more efficiently than complete objects.
Solution Approach 2:
The patent focuses computational resources on detecting and matching critical part configurations rather than exhaustively analyzing entire objects at all scales and viewpoints. This partial action approach achieves scale and viewpoint invariance through key part detection while maintaining processing efficiency.
Data Source
AI summary
A system and method are provided for learning part-based object models during a learning phase from training images and applying the learned object models to an input image during runtime. The learned part-based object models are augmented by appearance-based models of the objects. The part-based object models correspond to the shapes of the parts of an object. The appearance-based models provide additional appearance cues to the object models for object classification. The approach to learning part-based object models has the capability of learning object models without using viewpoint labels of the objects. The learning is also invariant to scale and in-plane rotation of the objects.