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Discriminative Supervised Hashing for Cross-Modal similarity Search
Jun Yu, Xiao-Jun Wu, Josef Kittler
With the advantage of low storage cost and high retrieval efficiency, hashing techniques have recently been an emerging topic in cross-modal similarity search. As multiple modal da…
Pose Invariant 3D Face Reconstruction
Lei Jiang, XiaoJun Wu, Josef Kittler
3D face reconstruction is an important task in the field of computer vision. Although 3D face reconstruction has being developing rapidly in recent years, it is still a challenge f…
MDLatLRR: A novel decomposition method for infrared and visible image fusion
Hui Li, Xiao-Jun Wu, Josef Kittler
Image decomposition is crucial for many image processing tasks, as it allows to extract salient features from source images. A good image decomposition method could lead to a bette…
Landmark Weighting for 3DMM Shape Fitting
Yu Yanga, Xiao-Jun Wu, Josef Kittler
Human face is a 3D object with shape and surface texture. 3D Morphable Model (3DMM) is a powerful tool for reconstructing the 3D face from a single 2D face image. In the shape fitt…
Learning Discriminative Hashing Codes for Cross-Modal Retrieval based on Multi-view Features
Jun Yu, Xiao-Jun Wu, Josef Kittler
Hashing techniques have been applied broadly in retrieval tasks due to their low storage requirements and high speed of processing. Many hashing methods based on a single view have…
Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Tracking
Tianyang Xu, Zhen-Hua Feng, Xiao-Jun Wu +1
With efficient appearance learning models, Discriminative Correlation Filter (DCF) has been proven to be very successful in recent video object tracking benchmarks and competitions…
Semi-supervised Hashing for Semi-Paired Cross-View Retrieval
Jun Yu, Xiao-Jun Wu, Josef Kittler
Recently, hashing techniques have gained importance in large-scale retrieval tasks because of their retrieval speed. Most of the existing cross-view frameworks assume that data are…
Grassmannian Discriminant Maps (GDM) for Manifold Dimensionality Reduction with Application to Image Set Classification
Rui Wang, Xiao-Jun Wu, Kai-Xuan Chen +1
In image set classification, a considerable progress has been made by representing original image sets on Grassmann manifolds. In order to extend the advantages of the Euclidean ba…
Infrared and Visible Image Fusion with ResNet and zero-phase component analysis
Hui Li, Xiao-Jun Wu, Tariq S. Durrani
Feature extraction and processing tasks play a key role in Image Fusion, and the fusion performance is directly affected by the different features and processing methods undertaken…
Riemannian kernel based Nyström method for approximate infinite-dimensional covariance descriptors with application to image set classification
Kai-Xuan Chen, Xiao-Jun Wu, Rui Wang +1
In the domain of pattern recognition, using the CovDs (Covariance Descriptors) to represent data and taking the metrics of the resulting Riemannian manifold into account have been…
Multiple Manifolds Metric Learning with Application to Image Set Classification
Rui Wang, Xiao-Jun Wu, Kai-Xuan Chen +1
In image set classification, a considerable advance has been made by modeling the original image sets by second order statistics or linear subspace, which typically lie on the Riem…
L1-(2D)2PCANet: A Deep Learning Network for Face Recognition
YunKun Li, XiaoJun Wu, Josef Kittler
In this paper, we propose a novel deep learning network L1-(2D)2PCANet for face recognition, which is based on L1-norm-based two-directional two-dimensional principal component ana…
A Simple Riemannian Manifold Network for Image Set Classification
Rui Wang, Xiao-Jun Wu, Josef Kittler
In the domain of image-set based classification, a considerable advance has been made by representing original image sets as covariance matrices which typical lie in a Riemannian m…
Multi-focus Noisy Image Fusion using Low-Rank Representation
Hui Li, Xiao-Jun Wu, Tariq Durrani
Multi-focus noisy image fusion represents an important task in the field of image fusion which generates a single, clear and focused image from all source images. In this paper, we…
Infrared and visible image fusion using Latent Low-Rank Representation
Hui Li, Xiao-Jun Wu
Infrared and visible image fusion is an important problem in the field of image fusion which has been applied widely in many fields. To better preserve the useful information from…
DenseFuse: A Fusion Approach to Infrared and Visible Images
Hui Li, Xiao-Jun Wu
In this paper, we present a novel deep learning architecture for infrared and visible images fusion problem. In contrast to conventional convolutional networks, our encoding networ…
Multi-focus Image Fusion using dictionary learning and Low-Rank Representation
Hui Li, Xiao-Jun Wu
Among the representation learning, the low-rank representation (LRR) is one of the hot research topics in many fields, especially in image processing and pattern recognition. Altho…
Infrared and Visible Image Fusion using a Deep Learning Framework
Hui Li, Xiao-Jun Wu, Josef Kittler
In recent years, deep learning has become a very active research tool which is used in many image processing fields. In this paper, we propose an effective image fusion method usin…