Optimization and Performance of a Template and Histogram based Image Classifier
Document Type
Oral Presentation
Campus where you would like to present
SURC 202
Start Date
17-5-2012
End Date
17-5-2012
Abstract
Within image classification tasks, many methods involve first extraction of low-level features and then use of these features to build a model that can be identified. This project evaluates one such classifier. The classifier uses the Harris Corner Detection algorithm to extract interest points within an image, and then uses these points to build a template that defines their spatial relationship. To provide additional information, multi-bin color and derivative histograms are used. This classifier contains several parameters that can be adjusted. The template uses a variable distance function; the histogram may vary in the number of bins, and these two components may be weighted differently in the final score. Therefore, appropriate methods are used to optimize these parameters. Finally, the optimized classifier is tested on images of different complexities.
Recommended Citation
Smigaj, James, "Optimization and Performance of a Template and Histogram based Image Classifier" (2012). Symposium Of University Research and Creative Expression (SOURCE). 21.
https://digitalcommons.cwu.edu/source/2012/oralpresentations/21
Additional Mentoring Department
Computer Science
Optimization and Performance of a Template and Histogram based Image Classifier
SURC 202
Within image classification tasks, many methods involve first extraction of low-level features and then use of these features to build a model that can be identified. This project evaluates one such classifier. The classifier uses the Harris Corner Detection algorithm to extract interest points within an image, and then uses these points to build a template that defines their spatial relationship. To provide additional information, multi-bin color and derivative histograms are used. This classifier contains several parameters that can be adjusted. The template uses a variable distance function; the histogram may vary in the number of bins, and these two components may be weighted differently in the final score. Therefore, appropriate methods are used to optimize these parameters. Finally, the optimized classifier is tested on images of different complexities.
Faculty Mentor(s)
Boris Kovalerchuk