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Published on: August 1, 2018
SEEMORE: combining color, shape, and texture histogramming in a neurally inspired approach to visual object
1Department of Biomedical Engineering, University of Southern California, Los Angeles 90089, USA.
Neural Computation
|May 15, 1997
Summary
The SEEMORE model, using a feedforward hierarchy of filters, achieved 97% accuracy in recognizing diverse 3D objects, supporting the feature-extraction hypothesis for brain object recognition.
Area of Science:
- Computational neuroscience
- Computer vision
- Machine learning
Background:
- The primate visual system's architecture suggests early object recognition relies on a feedforward feature-extraction hierarchy.
- Testing this conjecture requires robust computational models in complex visual domains.
Purpose of the Study:
- To evaluate the plausibility of a feedforward, feature-extraction model for object recognition in an engineering context.
- To assess the performance of the SEEMORE model on a challenging 3D object recognition task.
Main Methods:
- Developed a difficult 3D object recognition domain with 100 diverse objects (rigid, nonrigid, statistical) and complex scenes.
- Utilized the SEEMORE model, featuring 102 viewpoint-invariant nonlinear filters sensitive to contour, texture, and color.
- Trained SEEMORE on 12-36 views and tested on unnormalized views with variations in position, orientation, scale, and nonrigid deformations.
Main Results:
- Achieved 97% correct classification on 600 novel object views, significantly outperforming chance (1%).
- Demonstrated comparable performance on nonrigid objects and robustness to image degradations like occlusion, clutter, color shift, and noise.
- Observed emergent natural shape categories not explicitly encoded in the feature space.
Conclusions:
- The feedforward feature-space conjecture remains a compelling neurobiological model for early object recognition.
- The SEEMORE model's success suggests that hierarchical feature extraction is a viable computational strategy for complex visual tasks.
- The vast resources of the primate visual system may support even more sophisticated feedforward recognition capabilities.
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