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Feature selection based on rough sets and particle swarm optimization
TLDR
A new feature selection strategy based on rough sets and particle swarm optimization (PSO), which does not need complex operators such as crossover and mutation, and requires only primitive and simple mathematical operators, and is computationally inexpensive in terms of both memory and runtime. Expand
Patch Alignment for Dimensionality Reduction
TLDR
A new dimensionality reduction algorithm is developed, termed discrim inative locality alignment (DLA), by imposing discriminative information in the part optimization stage, and thorough empirical studies demonstrate the effectiveness of DLA compared with representative dimensionality Reduction algorithms. Expand
From Motion Blur to Motion Flow: A Deep Learning Solution for Removing Heterogeneous Motion Blur
TLDR
This work directly estimates the motion flow from the blurred image through a fully-convolutional deep neural network (FCN) and recovers the unblurred image from the estimated motion flow and is the first universal end-to-end mapping from the blur image to the dense motion flow. Expand
Linear local tangent space alignment and application to face recognition
TLDR
Since images of faces often belong to a manifold of intrinsically low dimension, the LLTSA algorithm for effective face manifold learning and recognition is developed, which achieves much higher recognition rates than a few competing methods. Expand
Discriminative Locality Alignment
TLDR
A new algorithm, termed Discriminative Locality Alignment (DLA), is proposed, which operates in the following three stages: first, in part optimization, discriminative information is imposed over patches, each of which is associated with one sample and its neighbors; then, in sample weighting, each part optimization is weighted by the margin degree, a measure of the importance of a given sample. Expand
Efficient Saliency-Model-Guided Visual Co-Saliency Detection
TLDR
Experimental results on two benchmark databases demonstrate that the proposed framework outperforms the state-of-the-art models in terms of both accuracy and efficiency. Expand
Seeing Deeply and Bidirectionally: A Deep Learning Approach for Single Image Reflection Removal
TLDR
This work argues that, to remove reflection truly well, it should estimate the reflection and utilize it to estimate the background image, and proposes a cascade deep neural network, which estimates both the Background image and the reflection. Expand
Robust Color Guided Depth Map Restoration
TLDR
This paper shows that using bicubic interpolated depth map can blur depth discontinuities when the upsampling factor is large and the input depth map contains large holes and heavy noise, and proposes a robust optimization framework for color guided depth map restoration that performs well in suppressing texture copy artifacts. Expand
Saliency propagation from simple to difficult
TLDR
A novel propagation algorithm employing the teaching- to-learn and learning-to-teach strategies is proposed to explicitly improve the propagation quality, demonstrating the superiority of the proposed algorithm over twelve representative saliency detectors. Expand
Gabor feature-based face recognition using supervised locality preserving projection
TLDR
A novel Gabor-based supervised locality preserving projection (GSLPP) method for face recognition using class labels of data points to enhance its discriminant power in their mapping into a low-dimensional space is introduced. Expand
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