Journal ArticleDOI
IMORL: Incremental Multiple-Object Recognition and Localization
Haibo He,Sheng Chen +1 more
TLDR
A neural network with a multilayer perceptron (MLP) structure as the base learning model is used and results show the effectiveness of this method in various video stream data sets.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.read more
Citations
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Journal ArticleDOI
Learning from Imbalanced Data
Haibo He,E.A. Garcia +1 more
TL;DR: A critical review of the nature of the problem, the state-of-the-art technologies, and the current assessment metrics used to evaluate learning performance under the imbalanced learning scenario is provided.
Journal ArticleDOI
Incremental Learning of Concept Drift in Nonstationary Environments
Ryan Elwell,Robi Polikar +1 more
TL;DR: An ensemble of classifiers-based approach for incremental learning of concept drift, characterized by nonstationary environments (NSEs), where the underlying data distributions change over time, which indicates that Learn++.NSE can track the changing environments very closely, regardless of the type of concept Drift.
Journal ArticleDOI
Medical image analysis
Baba C. Vemuri,James S. Duncan +1 more
TL;DR: Medical imaging systems: Physical principles and image reconstruction algorithms for magnetic resonance tomography, ultrasound and computer tomography (CT), and applications: Image enhancement, image registration, functional magnetic resonance imaging (fMRI).
Journal ArticleDOI
The Impact of Diversity on Online Ensemble Learning in the Presence of Concept Drift
TL;DR: A new categorization for concept drift is presented, separating drifts according to different criteria into mutually exclusive and nonheterogeneous categories, and it is shown that, before the drift, ensembles with less diversity obtain lower test errors, even though high diversity is more important for more severe drifts.
Journal ArticleDOI
DDD: A New Ensemble Approach for Dealing with Concept Drift
Leandro L. Minku,Xin Yao +1 more
TL;DR: DDD maintains ensembles with different diversity levels and is able to attain better accuracy than other approaches, outperforming other drift handling approaches in terms of accuracy when there are false positive drift detections.
References
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Proceedings ArticleDOI
PCA-SIFT: a more distinctive representation for local image descriptors
Yan Ke,Rahul Sukthankar +1 more
TL;DR: This paper examines (and improves upon) the local image descriptor used by SIFT, and demonstrates that the PCA-based local descriptors are more distinctive, more robust to image deformations, and more compact than the standard SIFT representation.
Proceedings ArticleDOI
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Is bottom-up attention useful for object recognition?
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Journal ArticleDOI
Medical image analysis
Baba C. Vemuri,James S. Duncan +1 more
TL;DR: Medical imaging systems: Physical principles and image reconstruction algorithms for magnetic resonance tomography, ultrasound and computer tomography (CT), and applications: Image enhancement, image registration, functional magnetic resonance imaging (fMRI).
Proceedings ArticleDOI
A Linear Programming Approach for Multiple Object Tracking
TL;DR: A linear programming relaxation scheme for the class of multiple object tracking problems where the inter-object interaction metric is convex and the intra-object term quantifying object state continuity may use any metric is found to be able to find the global optimum with high probability.
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