Journal of Algorithms
Distributed data fusion for real-time crowding estimation
Signal Processing
A Multilinear Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
Appearance based object modeling using texture database: acquisition, compression and rendering
EGRW '02 Proceedings of the 13th Eurographics workshop on Rendering
Multilinear Analysis of Image Ensembles: TensorFaces
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
TensorTextures: multilinear image-based rendering
ACM SIGGRAPH 2004 Papers
Handwritten digit classification using higher order singular value decomposition
Pattern Recognition
Estimating crowd density with Minkowski fractal dimension
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
Journal of Cognitive Neuroscience
A Tensor Approximation Approach to Dimensionality Reduction
International Journal of Computer Vision
Machine Vision and Applications
Are tensor decomposition solutions unique? on the Global convergence HOSVD and parafac algorithms
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
A neural-based crowd estimation by hybrid global learning algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Higher Order SVD Analysis for Dynamic Texture Synthesis
IEEE Transactions on Image Processing
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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This paper proposes a new method to estimate the crowd density based on the combination of higher-order singular value decomposition (HOSVD) and support vector machine (SVM). We first construct a higher-order tensor with all the images in the training set, and apply HOSVD to obtain a small set of orthonormal basis tensors that can span the principal subspace for all the training images. The coordinate, which best describes an image under this set of orthonormal basis tensors, is computed as the density character vector. Furthermore, a multi-class SVM classifier is designed to classify the extracted density character vectors into different density levels. Compared with traditional methods, we can make significant improvements to crowd density estimation. The experimental results show that the accuracy of our method achieves 96.33%, in which the misclassified images are all concentrated in their neighboring categories.