Visual Chunking: A List Prediction Framework for Region-Based Object Detection - Robotics Institute Carnegie Mellon University

Visual Chunking: A List Prediction Framework for Region-Based Object Detection

Conference Paper, Proceedings of (ICRA) International Conference on Robotics and Automation, pp. 5448 - 5454, May, 2015

Abstract

We consider detecting objects in an image by iteratively selecting from a set of arbitrarily shaped candidate regions. Our generic approach, which we term visual chunking, reasons about the locations of multiple object instances in an image while expressively describing object boundaries. We design an optimization criterion for measuring the performance of a list of such detections as a natural extension to a common per-instance metric. We present an efficient algorithm with provable performance for building a high- quality list of detections from any candidate set of region-based proposals. We also develop a simple class-specific algorithm to generate a candidate region instance in near-linear time in the number of low-level superpixels that outperforms other region generating methods. In order to make predictions on novel images at testing time without access to ground truth, we develop learning approaches to emulate these algorithms’ behaviors. We demonstrate that our new approach outperforms sophisticated baselines on benchmark datasets.

BibTeX

@conference{Rhinehart-2015-5963,
author = {Nicholas Rhinehart and Jiaji Zhou and Martial Hebert and J. Andrew (Drew) Bagnell},
title = {Visual Chunking: A List Prediction Framework for Region-Based Object Detection},
booktitle = {Proceedings of (ICRA) International Conference on Robotics and Automation},
year = {2015},
month = {May},
pages = {5448 - 5454},
}