Robust Method of Vote Aggregation and Proposition Verification for Invariant Local Features

TL;DR

This paper introduces a robust vote aggregation and verification method for local feature-based object detection, offering improved control, high detection rates, and reduced false positives over traditional clustering techniques.

Contribution

The paper proposes a novel vote aggregation approach with graphical presentation, proposition generation, iterative vote aggregation, and cascade filters, surpassing classic clustering methods.

Findings

High detection rate achieved

Low false detection rate demonstrated

Effective verification process implemented

Abstract

This paper presents a method for analysis of the vote space created from the local features extraction process in a multi-detection system. The method is opposed to the classic clustering approach and gives a high level of control over the clusters composition for further verification steps. Proposed method comprises of the graphical vote space presentation, the proposition generation, the two-pass iterative vote aggregation and the cascade filters for verification of the propositions. Cascade filters contain all of the minor algorithms needed for effective object detection verification. The new approach does not have the drawbacks of the classic clustering approaches and gives a substantial control over process of detection. Method exhibits an exceptionally high detection rate in conjunction with a low false detection chance in comparison to alternative methods.