On-line inverse multiple instance boosting for classifier grids
| Autor: | Peter M. Roth, Horst Bischof, Sabine Sternig |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2012 |
| Předmět: |
Boosting (machine learning)
Computer science Object detection Inverse 02 engineering and technology 010501 environmental sciences Machine learning computer.software_genre 01 natural sciences Article Artificial Intelligence Classifier grids 0202 electrical engineering electronic engineering information engineering AdaBoost 0105 earth and related environmental sciences business.industry Multiple instance learning On-line learning Pattern recognition Margin classifier Signal Processing 020201 artificial intelligence & image processing Artificial intelligence Computer Vision and Pattern Recognition business Classifier (UML) computer Software |
| Zdroj: | Pattern Recognition Letters |
| ISSN: | 0167-8655 |
| Popis: | Highlights ► Classifier grids showed excellent detection results for stationary cameras. ► On-line adaptive classifiers reduce the complexity of the classification task. ► Fixed update strategies allow long-term stability. ► Short-term stability through proposed inverse multiple instance learning. Classifier grids have shown to be a considerable choice for object detection from static cameras. By applying a single classifier per image location the classifier’s complexity can be reduced and more specific and thus more accurate classifiers can be estimated. In addition, by using an on-line learner a highly adaptive but stable detection system can be obtained. Even though long-term stability has been demonstrated such systems still suffer from short-term drifting if an object is not moving over a long period of time. The goal of this work is to overcome this problem and thus to increase the recall while preserving the accuracy. In particular, we adapt ideas from multiple instance learning (MIL) for on-line boosting. In contrast to standard MIL approaches, which assume an ambiguity on the positive samples, we apply this concept to the negative samples: inverse multiple instance learning. By introducing temporal bags consisting of background images operating on different time scales, we can ensure that each bag contains at least one sample having a negative label, providing the theoretical requirements. The experimental results demonstrate superior classification results in presence of non-moving objects. |
| Databáze: | OpenAIRE |
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