Object Recognition Using Shape Graph Quantization
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Solution Overview
Problem
Existing object recognition techniques face challenges in accurately identifying objects across varying imaging conditions due to intra-class variations, particularly for weakly-textured objects like apparel and furniture, leading to poor performance in visual searching and classification tasks.
Innovation Solution
The use of shape graphs to capture geometric configurations of tokens such as edges, contours, and interest points, with quantization of shape graphs into bins to create an object-shape model that leverages machine learning classifiers like support vector machines and randomized decision forests for improved recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If texture-based descriptors (SIFT, HoG) are used for object recognition, then recognition performance improves for textured objects, but recognition performance deteriorates for weakly-textured objects
Solution Approach 1:
The patent changes the fundamental parameters used for object recognition from texture-based features (gradients, frequencies) to shape-based features (geometric configurations, spatial relationships). By transforming the feature space from intensity-domain to geometry-domain, the system achieves reliable recognition for weakly-textured objects while maintaining performance for textured objects through shape invariance to illumination and texture variations.
Solution Approach 2:
The patent substitutes the mechanical/physical approach of texture analysis (relying on surface properties, gradients, and frequencies) with a geometric approach (relying on spatial configurations and shape relationships). This substitution replaces the dependency on textural mechanics with shape-based geometry, enabling robust recognition across diverse object types including those with minimal texture.
2Reliability
If whole contours are captured for shape-based recognition, then shape information is preserved, but computational complexity and inference cost increase
Solution Approach 1:
The patent segments the continuous contour into discrete sampled points, transforming the problem from processing entire continuous boundaries to analyzing finite point configurations. This segmentation enables the use of computationally efficient algorithms while preserving essential shape characteristics through strategic point sampling and geometric relationship analysis.
Solution Approach 2:
The patent extracts key geometric features from complete contours by sampling representative points and computing their relative configurations. Instead of processing all contour information, the method extracts essential geometric relationships (distances, angles, orientations) between sampled points, reducing computational load while maintaining recognition accuracy.
3Ease of operation
If simple similarity measures (Euclidean distance) are used in image space, then computational simplicity is maintained, but recognition accuracy deteriorates due to intra-class variations
Solution Approach 1:
The patent transitions from measuring similarity in image space (intensity domains) to measuring similarity in shape space (geometric configuration domains). By changing the dimensionality of the feature space from pixel intensities to geometric relationships between sampled points, the system achieves both computational efficiency and high recognition accuracy by capturing invariant shape properties that transcend illumination and pose variations.
Data Source
AI summary
Techniques for a shape descriptor used for object recognition are described. Tokens of an object in digital image data are captured, where tokens can be edges, interest points or even parts. Geometric configurations of the tokens are captured by describing portions of the shape of the object. The shape of such configurations is finely quantized and each configuration from the image is assigned to a quantization bin. Objects are recognized by utilizing a number of quantization bins as features. This Abstract is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims.