Efficient Multiple Kernel Classification Using Feature and Decision Level Fusion

Pinar, Anthony J.; Rice, Joseph; Hu, Lequn; Anderson, Derek T.; Havens, Timothy C.

doi:10.1109/tfuzz.2016.2633372

Cited by 46 publications

(45 citation statements)

References 43 publications

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“…Decision-level fusion can be applied by using multiple SVMs and fusing the results. Multiple Kernel Learning (MKL) [29,30] can also be applied, which searches for an optimal linear combination of kernels which are utilized in multiple SVMs.…”

Section: Discussionmentioning

confidence: 99%

Screening Mississippi River Levees Using Texture-Based and Polarimetric-Based Features from Synthetic Aperture Radar Data

et al. 2017

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Abstract:This article reviews the use of synthetic aperture radar remote sensing data for earthen levee mapping with an emphasis on finding the slump slides on the levees. Earthen levees built on the natural levees parallel to the river channel are designed to protect large areas of populated and cultivated land in the Unites States from flooding. One of the signs of potential impending levee failure is the appearance of slump slides. On-site inspection of levees is expensive and time-consuming; therefore, a need to develop efficient techniques based on remote sensing technologies is mandatory to prevent failures under flood loading. Analysis of multi-polarized radar data is one of the viable tools for detecting the problem areas on the levees. In this study, we develop methods to detect anomalies on the levee, such as slump slides and give levee managers new tools to prioritize their tasks. This paper presents results of applying the National Aeronautics and Space Administration (NASA) Jet Propulsion Lab (JPL)'s Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) quad-polarized L-band data to detect slump slides on earthen levees. The study area encompasses a portion of levees of the lower Mississippi River in the United States. In this paper, we investigate the performance of polarimetric and texture features for efficient levee classification. Texture features derived from the gray level co-occurrence (GLCM) matrix and discrete wavelet transform were computed and analyzed for efficient levee classification. The pixel-based polarimetric decomposition features, such as entropy, anisotropy, and scattering angle were also computed and applied to the support vector machine classifier to characterize the radar imagery and compared the results with texture-based classification. Our experimental results showed that inclusion of textural features derived from the SAR data using the discrete wavelet transform (DWT) features and GLCM features provided higher overall classification accuracies compared to the pixel-based polarimetric features.

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Section: Discussionmentioning

confidence: 99%

Screening Mississippi River Levees Using Texture-Based and Polarimetric-Based Features from Synthetic Aperture Radar Data

et al. 2017

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“…These ensemble methods have been very popular in the machine learning community due to their ability of producing more accurate results than individual classifiers [2] in a very wide range of application areas [3]. Another approach to obtain ensemble classification is the use of the Fuzzy Integral (FI) aggregation defined with respect to a Fuzzy Measure (FM) [4][5][6][7][8]. The FI is a non-linear aggregation operator to fuse weighted information from multiple sources, where the weights are captured by a FM.…”

Section: Introductionmentioning

confidence: 99%

“…The FM not only captures the worth of the individual sources, but also the weights of all subset of sources. Recently, a FM-FI based ensemble classification algorithm Decisionlevel Fuzzy Integral Multiple Kernel Learning (DeFIMKL) [4] was introduced, which aggregates the results of kernel-SVMs through the use of Choquet Fuzzy Integral (CFI) with respect to a FM learned by a regularised quadratic programming approach. Upon initial investigation in [4], [9], the accuracy of FI-FM based ensemble classification method were found to be better than classifiers based on multiple kernel learning.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, a FM-FI based ensemble classification algorithm Decisionlevel Fuzzy Integral Multiple Kernel Learning (DeFIMKL) [4] was introduced, which aggregates the results of kernel-SVMs through the use of Choquet Fuzzy Integral (CFI) with respect to a FM learned by a regularised quadratic programming approach. Upon initial investigation in [4], [9], the accuracy of FI-FM based ensemble classification method were found to be better than classifiers based on multiple kernel learning. These works further concluded that DeFIMKL was the best among decision level fusion based FI-FM based ensemble classifiers and thus it has been selected in the work as representative of the FI-FM based ensemble classifier family.…”

Section: Introductionmentioning

confidence: 99%

“…These works further concluded that DeFIMKL was the best among decision level fusion based FI-FM based ensemble classifiers and thus it has been selected in the work as representative of the FI-FM based ensemble classifier family. However, no in-depth comparison of the FI-FM based ensemble classifier with other ensemble methods have been found in the literature [4][5][6][7][8][9] (discussed in detail in section 2.3). Thus, the motivation of this study is to determine the performance of FM-FI based ensemble methods for the purpose of ensemble classification.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Fuzzy Integral-Fuzzy Measure Based Ensemble Algorithms with the State-of-the-Art Ensemble Algorithms

Agrawal

Pinar

Wagner

et al. 2018

Communications in Computer and Information Science

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Abstract. The Fuzzy Integral (FI) is a non-linear aggregation operator which enables the fusion of information from multiple sources in respect to a Fuzzy Measure (FM) which captures the worth of both the individual sources and all their possible combinations. Based on the expected potential of non-linear aggregation offered by the FI, its application to decision-level fusion in ensemble classifiers, i.e. to fuse multiple classifiers outputs towards one superior decision level output, has recently been explored. A key example of such a FI-FM ensemble classification method is the Decision-level Fuzzy Integral Multiple Kernel Learning (DeFIMKL) algorithm, which aggregates the outputs of kernel based classifiers through the use of the Choquet FI with respect to a FM learned through a regularised quadratic programming approach. While the approach has been validated against a number of classifiers based on multiple kernel learning, it has thus far not been compared to the state-of-the-art in ensemble classification. Thus, this paper puts forward a detailed comparison of FI-FM based ensemble methods, specifically the DeFIMKL algorithm, with state-of-the art ensemble methods including Adaboost, Bagging, Random Forest and Majority Voting over 20 public datasets from the UCI machine learning repository. The results on the selected datasets suggest that the FI based ensemble classifier performs both well and efficiently, indicating that it is a viable alternative when selecting ensemble classifiers and indicating that the non-linear fusion of decision level outputs offered by the FI provides expected potential and warrants further study.

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Event and Activity Recognition in Video Surveillance for Cyber-Physical Systems

Bhaumik¹,

Jana

Mohanta

2021

Advances in Science, Technology &Amp; Innovation

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In this chapter, we aim to aid the development of Cyber-Physical Systems (CPS) in automated understanding of events and activities in various applications of videosurveillance. These events are mostly captured by drones, CCTVs or novice and unskilled individuals on low-end devices. Being unconstrained in nature, these videos are immensely challenging due to a number of quality factors. We present an extensive account of the various approaches taken to solve the problem over the years. This ranges from methods as early as Structure from Motion (SFM) based approaches to recent solution frameworks involving deep neural networks. We show that the long-term motion patterns alone play a pivotal role in the task of recognizing an event. Consequently each video is significantly represented by a fixed number of key-frames using a graph-based approach. Only the temporal features are exploited using a hybrid Convolutional Neural Network (CNN) + Recurrent Neural Network (RNN) architecture. The results we obtain are encouraging as they outperform standard temporal CNNs and are at par with those using spatial information along with motion cues. Further exploring multistream models, we conceive a multi-tier fusion strategy for the spatial and temporal wings of a network. A consolidated representation of the respective individual prediction vectors on video and frame levels is obtained

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Efficient Multiple Kernel Classification Using Feature and Decision Level Fusion

Cited by 46 publications

References 43 publications

Screening Mississippi River Levees Using Texture-Based and Polarimetric-Based Features from Synthetic Aperture Radar Data

Screening Mississippi River Levees Using Texture-Based and Polarimetric-Based Features from Synthetic Aperture Radar Data

Comparison of Fuzzy Integral-Fuzzy Measure Based Ensemble Algorithms with the State-of-the-Art Ensemble Algorithms

Event and Activity Recognition in Video Surveillance for Cyber-Physical Systems

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