Reliable Attribute-Based Object Recognition Using High Predictive Value Classifiers

TL;DR

This paper introduces a robust attribute-based object recognition method using classifiers with multiple thresholds, incorporating viewing conditions and uncertainty handling, validated through empirical studies on point-cloud data.

Contribution

It presents a novel approach that fuses multiple classifiers considering distance and employs dual thresholds for improved recognition accuracy.

Findings

Enhanced recognition accuracy with distance-aware classifier fusion

Effective handling of uncertain classifications through dual thresholds

Empirical validation demonstrates practical feasibility

Abstract

We consider the problem of object recognition in 3D using an ensemble of attribute-based classifiers. We propose two new concepts to improve classification in practical situations, and show their implementation in an approach implemented for recognition from point-cloud data. First, the viewing conditions can have a strong influence on classification performance. We study the impact of the distance between the camera and the object and propose an approach to fuse multiple attribute classifiers, which incorporates distance into the decision making. Second, lack of representative training samples often makes it difficult to learn the optimal threshold value for best positive and negative detection rate. We address this issue, by setting in our attribute classifiers instead of just one threshold value, two threshold values to distinguish a positive, a negative and an uncertainty class, and we prove the theoretical correctness of this approach. Empirical studies demonstrate the effectiveness and feasibility of the proposed concepts.