Robustness of Regional Matching Scheme over Global Matching Scheme

TL;DR

This paper proves that bottom-up regional matching is more stable and robust against noise than global matching, supported by theoretical analysis and experiments on image recognition tasks.

Contribution

It establishes a theoretical framework showing regional matching's superior robustness over global matching against concentrated noise in image recognition.

Findings

Regional matching has higher noise tolerance than global matching.

Theoretical bounds demonstrate greater stability of regional matching.

Experimental validation on facial and flag recognition supports the theory.

Abstract

The paper has established and verified the theory prevailing widely among image and pattern recognition specialists that the bottom-up indirect regional matching process is the more stable and the more robust than the global matching process against concentrated types of noise represented by clutter, outlier or occlusion in the imagery. We have demonstrated this by analyzing the effect of concentrated noise on a typical decision making process of a simplified two candidate voting model where our theorem establishes the lower bounds to a critical breakdown point of election (or decision) result by the bottom-up matching process are greater than the exact bound of the global matching process implying that the former regional process is capable of accommodating a higher level of noise than the latter global process before the result of decision overturns. We present a convincing experimental verification supporting not only the theory by a white-black flag recognition problem in the presence of localized noise but also the validity of the conjecture by a facial recognition problem that the theorem remains valid for other decision making processes involving an important dimension-reducing transform such as principal component analysis or a Gabor transform.