Issue No. 11 - November (1997 vol. 19)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.632991
<p><b>Abstract</b>—We present five performance measures to evaluate grouping modules in the context of constrained search and indexing based object recognition. Using these measures, we demonstrate a sound experimental framework, based on statistical ANOVA tests, to compare and contrast three edge based organization modules, namely, those of Etemadi et al., Jacobs, and Sarkar-Boyer in the domain of aerial objects using 50 images. With adapted parameters, the Jacobs module performs overall the best for constraint based recognition. For fixed parameters, the Sarkar-Boyer module is the best in terms of recognition accuracy and indexing speedup. Etemadi et al.'s module performs equally well with fixed and adapted parameters while the Jacobs module is most sensitive to fixed and adapted parameter choices. The overall performance ranking of the modules is Jacobs, Sarkar-Boyer, and Etemadi et al.</p>
Perceptual organization, performance evaluation, analysis of variance, ANOVA, experimental vision, intermediate level computer vision, feature grouping, performance characterization.
S. Borra and S. Sarkar, "A Framework for Performance Characterization of Intermediate-Level Grouping Modules," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 19, no. , pp. 1306-1312, 1997.