Multi-class batch-mode active learning for image classification

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 different 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.

Authors

• Ajay J. Joshi
• Fatih Porikli
• Nikolaos P. Papanikolopoulos

Keywords

• Image classification
• Humans
• Robot vision systems
• Training data
• Robotics and automation
• Image recognition
• Object recognition
• USA Councils
• Cities and towns
• Surveillance
• Active Learning
• Batch-mode Active Learning
• Multi-label
• Computationally Intractable
• Image Classification Accuracy
• Training Set
• Support Vector Machine
• Similarity Measure
• Binary Classification
• Image Segmentation
• Random Selection
• Probability Estimates
• Selection Algorithm
• Use Of Scores
• Classification Rate
• Improve Classification Accuracy
• Nondecreasing Function
• Diverse Training
• Unlabeled Examples
• Jensen-Shannon Divergence
• Redundant Measurements
• Large Training Set
• Interference Scores
• Information-theoretic Measures
• Image Classification Problems