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Multiple Instance Learning with Random Forests and Applications in Industrial Optical Inspection

Wieler, Matthias

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Automatic defect detection in industrial optical inspection requires algorithms that can learn from data. A special challenge is data with incomplete labels. One of the methods that the field of machine learning has brought forth to deal with incomplete labels is multiple instance learning. One trait of this setting is that it groups datapoints (instances) into bags.

We propose a novel method to predict bag probabilities from given instance probabilities that has the advantage that its results do not depend on bag size. Also, we propose an extension of the multiple instance model that allows the user to steer the number of instances that are classified as positive.

We implement these methods with an algorithm based on the well-known random forest classifier. Results on a standard benchmark dataset show competitive performance. Furthermore, we apply this algorithm to image data that reflects the challenges of industrial optical inspection, and we show that in this setting it improves over the standard random forest.

Item Type: Dissertation
Supervisor: Hamprecht, Prof. Dr. Fred A.
Date of thesis defense: 23 July 2014
Date Deposited: 11 Sep 2014 07:35
Date: 2014
Faculties / Institutes: The Faculty of Physics and Astronomy > Dekanat der Fakultät für Physik und Astronomie
Service facilities > Interdisciplinary Center for Scientific Computing
Subjects: 004 Data processing Computer science
310 General statistics
670 Manufacturing
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