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IMORL: Incremental Multiple-Object Recognition and Localization
- Source :
- IEEE Transactions on Neural Networks. 19:1727-1738
- Publication Year :
- 2008
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2008.
-
Abstract
- This paper proposes an incremental multiple-object recognition and localization (IMORL) method. The objective of IMORL is to adaptively learn multiple interesting objects in an image. Unlike the conventional multiple-object learning algorithms, the proposed method can automatically and adaptively learn from continuous video streams over the entire learning life. This kind of incremental learning capability enables the proposed approach to accumulate experience and use such knowledge to benefit future learning and the decision making process. Furthermore, IMORL can effectively handle variations in the number of instances in each data chunk over the learning life. Another important aspect analyzed in this paper is the concept drifting issue. In multiple-object learning scenarios, it is a common phenomenon that new interesting objects may be introduced during the learning life. To handle this situation, IMORL uses an adaptive learning principle to autonomously adjust to such new information. The proposed approach is independent of the base learning models, such as decision tree, neural networks, support vector machines, and others, which provide the flexibility of using this method as a general learning methodology in multiple-object learning scenarios. In this paper, we use a neural network with a multilayer perceptron (MLP) structure as the base learning model and test the performance of this method in various video stream data sets. Simulation results show the effectiveness of this method.
- Subjects :
- Proactive learning
Wake-sleep algorithm
Computer Networks and Communications
Computer science
Active learning (machine learning)
Competitive learning
Stability (learning theory)
Multi-task learning
Semi-supervised learning
Machine learning
computer.software_genre
Robot learning
Pattern Recognition, Automated
Artificial Intelligence
Leabra
Image Interpretation, Computer-Assisted
Instance-based learning
Learning classifier system
Artificial neural network
business.industry
Algorithmic learning theory
Online machine learning
General Medicine
Image Enhancement
Generalization error
Computer Science Applications
Support vector machine
Computational learning theory
Multilayer perceptron
Incremental learning
Unsupervised learning
Artificial intelligence
Adaptive learning
business
Feature learning
computer
Algorithms
Software
Subjects
Details
- ISSN :
- 19410093 and 10459227
- Volume :
- 19
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Neural Networks
- Accession number :
- edsair.doi.dedup.....619cc31a2c45d08c6fcbc25c4998828a