TR2010-034

Multi-Class Batch-Mode Active Learning for Image Classification


Abstract:

Accurate image classification is crucial in many robotics and surveillance applications - for example, a vision system on a robot needs to accurately recognize the objects seen by its camera. Object recognition systems typically need a large amount of training data for satisfactory performance. The problem is particularly acute when many object categories are present. In this paper we present a batch-mode active learning framework for multi-class image classification systems. In active learning, images are to be chosen for interactive labeling, instead of passively accepting training data. Our framework addresses two important issues: i) it handles redundancy between images which is crucial when batch-mode selection is performed; and ii) we pose batch selection as a submodular function optimization problem that makes an inherently intractable problem efficient to solve, while having approximation guarantees. We show results on image classification data in which our approach substantially reduces the amount of training required over the baseline.

 

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    •  NEWS    ICRA 2010: 3 publications by Yuichi Taguchi, Amit K. Agrawal, C. Oncel Tuzel, Tim K. Marks and others
      Date: May 3, 2010
      Where: IEEE International Conference on Robotics and Automation (ICRA)
      MERL Contact: Tim K. Marks
      Research Area: Computer Vision
      Brief
      • The papers "Pose Estimation in Heavy Clutter Using a Multi-Flash Camera" by Liu, M.-Y., Tuzel, C.O., Veeraraghavan, A.N., Chellappa, R., Agrawal, A.K. and Okuda, H., "Rao-Blackwellized Particle Filtering for Probing-based 6-DOF Localization in Robotic Assembly" by Taguchi, Y., Marks, T.K. and Okuda, H. and "Multi-Class Batch-mode Active Learning for Image Classification" by Joshi, A.J., Porikli, F. and Papanikolopoulos, N. were presented at the IEEE International Conference on Robotics and Automation (ICRA).
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