A Classification Leveraged Object Detector

TL;DR

This paper introduces a novel method that enhances object detection performance by leveraging image classification on regions identified by a preliminary detector, achieving improved average precision on standard datasets.

Contribution

It presents a simple, principled approach to improve object detection by integrating classification on supporting regions, boosting performance over existing detectors.

Findings

Improved average precision from 35.9% to 39.5% on PASCAL VOC 2007.

Leveraged classification to enhance object detection performance.

Demonstrated effectiveness of the approach on standard benchmarks.

Abstract

Currently, the state-of-the-art image classification algorithms outperform the best available object detector by a big margin in terms of average precision. We, therefore, propose a simple yet principled approach that allows us to leverage object detection through image classification on supporting regions specified by a preliminary object detector. Using a simple bag-of- words model based image classification algorithm, we leveraged the performance of the deformable model objector from 35.9% to 39.5% in average precision over 20 categories on standard PASCAL VOC 2007 detection dataset.