Layered Logic Classifiers: Exploring the `And' and `Or' Relations

TL;DR

This paper introduces layered logic classifiers that combine 'and', 'or', and 'not' operations to handle complex pattern distributions, demonstrating improved performance and efficiency over traditional methods in various datasets and vision tasks.

Contribution

The paper presents a novel layered logic classifier framework that effectively models complex patterns using logical operations, offering a general, easy-to-implement alternative to existing classifiers.

Findings

Significant performance improvements over AdaBoost on multiple datasets.

Reduced training complexity compared to decision tree-based AdaBoost.

Effective in diverse applications like object segmentation and pedestrian detection.

Abstract

Designing effective and efficient classifier for pattern analysis is a key problem in machine learning and computer vision. Many the solutions to the problem require to perform logic operations such as `and', `or', and `not'. Classification and regression tree (CART) include these operations explicitly. Other methods such as neural networks, SVM, and boosting learn/compute a weighted sum on features (weak classifiers), which weakly perform the 'and' and 'or' operations. However, it is hard for these classifiers to deal with the 'xor' pattern directly. In this paper, we propose layered logic classifiers for patterns of complicated distributions by combining the `and', `or', and `not' operations. The proposed algorithm is very general and easy to implement. We test the classifiers on several typical datasets from the Irvine repository and two challenging vision applications, object segmentation and pedestrian detection. We observe significant improvements on all the datasets over the widely used decision stump based AdaBoost algorithm. The resulting classifiers have much less training complexity than decision tree based AdaBoost, and can be applied in a wide range of domains.