1,560 research outputs found
Foreword
Foreword by the Provost and Senior Vice President for Academic Affairs, Cleveland State Universit
Foreword
Foreword by the Provost and Senior Vice President for Academic Affairs, Cleveland State Universit
Bounded-Distortion Metric Learning
Metric learning aims to embed one metric space into another to benefit tasks
like classification and clustering. Although a greatly distorted metric space
has a high degree of freedom to fit training data, it is prone to overfitting
and numerical inaccuracy. This paper presents {\it bounded-distortion metric
learning} (BDML), a new metric learning framework which amounts to finding an
optimal Mahalanobis metric space with a bounded-distortion constraint. An
efficient solver based on the multiplicative weights update method is proposed.
Moreover, we generalize BDML to pseudo-metric learning and devise the
semidefinite relaxation and a randomized algorithm to approximately solve it.
We further provide theoretical analysis to show that distortion is a key
ingredient for stability and generalization ability of our BDML algorithm.
Extensive experiments on several benchmark datasets yield promising results
FAS-UNet: A Novel FAS-driven Unet to Learn Variational Image Segmentation
Solving variational image segmentation problems with hidden physics is often
expensive and requires different algorithms and manually tunes model parameter.
The deep learning methods based on the U-Net structure have obtained
outstanding performances in many different medical image segmentation tasks,
but designing such networks requires a lot of parameters and training data, not
always available for practical problems. In this paper, inspired by traditional
multi-phase convexity Mumford-Shah variational model and full approximation
scheme (FAS) solving the nonlinear systems, we propose a novel
variational-model-informed network (denoted as FAS-Unet) that exploits the
model and algorithm priors to extract the multi-scale features. The proposed
model-informed network integrates image data and mathematical models, and
implements them through learning a few convolution kernels. Based on the
variational theory and FAS algorithm, we first design a feature extraction
sub-network (FAS-Solution module) to solve the model-driven nonlinear systems,
where a skip-connection is employed to fuse the multi-scale features. Secondly,
we further design a convolution block to fuse the extracted features from the
previous stage, resulting in the final segmentation possibility. Experimental
results on three different medical image segmentation tasks show that the
proposed FAS-Unet is very competitive with other state-of-the-art methods in
qualitative, quantitative and model complexity evaluations. Moreover, it may
also be possible to train specialized network architectures that automatically
satisfy some of the mathematical and physical laws in other image problems for
better accuracy, faster training and improved generalization.The code is
available at \url{https://github.com/zhuhui100/FASUNet}.Comment: 18 page
Coupled Multiple Kernel Learning for Supervised Classification
Multiple kernel learning (MKL) has recently received significant attention due to the fact that it is able to automatically fuse information embedded in multiple base kernels and then find a new kernel for classification or regression. In this paper, we propose a coupled multiple kernel learning method for supervised classification (CMKL-C), which comprehensively involves the intra-coupling within each kernel, inter-coupling among different kernels and coupling between target labels and real ones in MKL. Specifically, the intra-coupling controls the class distribution in a kernel space, the inter-coupling captures the co-information of base kernel matrices, and the last type of coupling determines whether the new learned kernel can make a correct decision. Furthermore, we deduce the analytical solutions to solve the CMKL-C optimization problem for highly efficient learning. Experimental results over eight UCI data sets and three bioinformatics data sets demonstrate the superior performance of CMKL-C in terms of the classification accuracy
Video anomaly detection and localization by local motion based joint video representation and OCELM
Nowadays, human-based video analysis becomes increasingly exhausting due to the ubiquitous use of surveillance cameras and explosive growth of video data. This paper proposes a novel approach to detect and localize video anomalies automatically. For video feature extraction, video volumes are jointly represented by two novel local motion based video descriptors, SL-HOF and ULGP-OF. SL-HOF descriptor captures the spatial distribution information of 3D local regions’ motion in the spatio-temporal cuboid extracted from video, which can implicitly reflect the structural information of foreground and depict foreground motion more precisely than the normal HOF descriptor. To locate the video foreground more accurately, we propose a new Robust PCA based foreground localization scheme. ULGP-OF descriptor, which seamlessly combines the classic 2D texture descriptor LGP and optical flow, is proposed to describe the motion statistics of local region texture in the areas located by the foreground localization scheme. Both SL-HOF and ULGP-OF are shown to be more discriminative than existing video descriptors in anomaly detection. To model features of normal video events, we introduce the newly-emergent one-class Extreme Learning Machine (OCELM) as the data description algorithm. With a tremendous reduction in training time, OCELM can yield comparable or better performance than existing algorithms like the classic OCSVM, which makes our approach easier for model updating and more applicable to fast learning from the rapidly generated surveillance data. The proposed approach is tested on UCSD ped1, ped2 and UMN datasets, and experimental results show that our approach can achieve state-of-the-art results in both video anomaly detection and localization task.This work was supported by the National Natural Science Foundation of China (Project nos. 60970034, 61170287, 61232016)
- …