Information Subspace-Based Fusion for Vehicle Classification

Ghanem, Sally; Panahi, Ashkan; Krim, Hamid; Kerekes, Ryan A.; Mattingly, John

doi:10.23919/eusipco.2018.8553445

Cited by 7 publications

(4 citation statements)

References 24 publications

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“…Uncovering the principles and laying out the fundamentals for multi-modal data has become an important topic in research in light of many applications in diverse fields including image fusion [8], target recognition [9][10][11][12], speaker recognition [13], and handwriting analysis [14]. Convolutional neural networks have been widely used on multi-modal data as in [15][16][17][18][19].…”

Section: Related Workmentioning

confidence: 99%

Scaling Subspace-Driven Approaches Using Information Fusion

Ghanem¹,

Krim²

2023

Artificial Intelligence

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In this work, we seek to exploit the deep structure of multi-modal data to robustly exploit the group subspace distribution of the information using the Convolutional Neural Networks (CNNs) formalism. Upon unfolding the set of subspaces constituting each data modality, and learning their corresponding encoders, an optimized integration of the generated inherent information is carried out to yield a characterization of various classes. Referred to as deep Multimodal Robust Group Subspace Clustering (DRoGSuRe), this approach is compared against the independently developed state-of-the-art approach named Deep Multimodal Subspace Clustering (DMSC). Experiments on different multimodal datasets show that our approach is competitive and more robust in the presence of noise.

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Section: Related Workmentioning

confidence: 99%

Scaling Subspace-Driven Approaches Using Information Fusion

Ghanem¹,

Krim²

2023

Artificial Intelligence

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“…The topic of multi-modal data fusion has been extensively studied in computer vision. Laying out the fundamentals for data fusion has become crucial for many applications, including target recognition [ 7 , 8 , 9 ], handwriting analysis [ 10 ], and image fusion [ 11 ]. A comprehensive survey of data fusion is provided in [ 12 , 13 ].…”

Section: Related Workmentioning

confidence: 99%

Decision-Based Fusion for Vehicle Matching

Ghanem

Kerekes

Tokola

2022

Sensors

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In this work, a framework is proposed for decision fusion utilizing features extracted from vehicle images and their detected wheels. Siamese networks are exploited to extract key signatures from pairs of vehicle images. Our approach then examines the extent of reliance between signatures generated from vehicle images to robustly integrate different similarity scores and provide a more informed decision for vehicle matching. To that end, a dataset was collected that contains hundreds of thousands of side-view vehicle images under different illumination conditions and elevation angles. Experiments show that our approach could achieve better matching accuracy by taking into account the decisions made by a whole-vehicle or wheels-only matching network.

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“…This paradigm has broadly been adopted in many applications such as image segmentation Yang, Rao and Ma (2006), image compression Hong, Wright, Huang and Ma (2006), and object clustering Ho, Yang, Lim, Lee and Kriegman (2003). Uncovering the principles and laying out the fundamentals for multi-modal data has become an important topic in research in light of many applications in diverse fields including image fusion Hellwich and Wiedemann (2000), target recognition Korona and Kokar (1996), Ghanem, Panahi, Krim, Kerekes and Mattingly (2018), Ghanem, Roheda and Krim (2021), Wang, Skau, Krim and Cervone (2018), speaker recognition Soong and Rosenberg (1988), and handwriting analysis Xu, Krzyzak and Suen (1992). Convolutional neural networks have been widely used on multi-modal data as in Ngiam, Khosla, Kim, Nam, Lee and Ng (2011) Ramachandram and Taylor (2017), Valada, Oliveira, Brox and Burgard (2016), Roheda, Riggan, Krim and Dai (2018b), and Roheda, Krim, Luo and Wu (2018a).…”

Section: Related Workmentioning

confidence: 99%

Information Fusion: Scaling Subspace-Driven Approaches

Ghanem¹,

Krim²

2022

Preprint

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In this work, we seek to exploit the deep structure of multi-modal data to robustly exploit the group subspace distribution of the information using the Convolutional Neural Network (CNN) formalism. Upon unfolding the set of subspaces constituting each data modality, and learning their corresponding encoders, an optimized integration of the generated inherent information is carried out to yield a characterization of various classes. Referred to as deep Multimodal Robust Group Subspace Clustering (DRoGSuRe), this approach is compared against the independently developed state-of-the-art approach named Deep Multimodal Subspace Clustering (DMSC). Experiments on different multimodal datasets show that our approach is competitive and more robust in the presence of noise.

show abstract

Information Subspace-Based Fusion for Vehicle Classification

Cited by 7 publications

References 24 publications

Scaling Subspace-Driven Approaches Using Information Fusion

Scaling Subspace-Driven Approaches Using Information Fusion

Decision-Based Fusion for Vehicle Matching

Information Fusion: Scaling Subspace-Driven Approaches

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