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This paper proposes the Anchored Kernel Metric Learning (AKML) method, which learns metrics by sparsely combining locally discriminative rank-1 basis matrices in the anchored kernel induced space. First, we apply k-means clustering to generate anchored samples, thus forming an anchored sample matrix. Based on the assumption that the basis matrices can be formulated as linear combinations of anchored...
This paper proposes a novel model for contrast enhancement of RGB images. The average local contrast measure is increased within a variational framework which preserves the hue of the original image by coupling the channels. The user is enabled to intuitively control the level of the contrast as well as the scale of the enhanced details. Moreover, our model avoids large modifications of the original...
The major challenge of machine learning based image steganalysis lies in obtaining powerful feature representations. Recently, Qian et al. have shown that Convolutional Neural Network (CNN) is effective for learning features automatically for steganalysis. In this paper, we follow up this new paradigm in steganalysis, and propose a framework based on transfer learning to help the training of CNN for...
Document is unavailable: This DOI was registered to an article that was not presented by the author(s) at this conference. As per section 8.2.1.B.13 of IEEE's "Publication Services and Products Board Operations Manual," IEEE has chosen to exclude this article from distribution. We regret any inconvenience.
Scene depth variation is an important factor that leads to spatially-varying camera motion blur. Most of the previous methods require auxiliary cameras or user interaction to make depth-aware deblurring tractable. In this work, we propose to use a noisy/blurred/noisy image sequence and simultaneously recorded inertial measurements to jointly estimate scene depth and remove spatially-varying blur caused...
In this paper, we propose a new Multi-kernel Metric Learning (MKML) approach to enhance the performance of person re-identification using adaptive weighted Multi-kernel. The intuition behind our approach is that different features, i.e., low-level and middle-level features, have different nature and thus discriminating capability, utilizing different kernels could map these features into sub-spaces,...
Power line inspection is an essential but costly task while automated UAV (unmanned aerial vehicle) inspection can greatly reduce such costs. However, navigating along power lines is a challenging task due to the narrow width and limited features of power lines. Existing power line tracking methods have threshold selection problems and cannot work well for complex and changing backgrounds. We make...
We study the problem of scene classification for RGB-D images in this paper. Firstly we analyze the difference between the RGB and depth images. And then based on the difference, an efficient method is implemented to make use of the RGB and depth images and make a well fusion for the RGB and depth features. Focusing on the difference of modality between the RGB and depth images, we propose a method...
In recent years, correlation filter based trackers outperform better than other trackers. Nevertheless, they only employ one feature and a single kernel, so they are usually not robust in complex scenes. In this paper, we derive a multi-feature and multi-kernel correlation filter based tracker which fully takes advantage of the invariance-discriminative power spectrums of various features and kernels...
When applying a filter to an image, it often makes practical sense to maintain the local brightness level from input to output image. This is achieved by normalizing the filter coefficients so that they sum to one. This concept is generally taken for granted, but is particularly important where nonlinear filters such as the bilateral or and non-local means are concerned, where the effect on local...
We propose a novel approach for pixel classification in hyperspectral images, leveraging on both the spatial and spectral information in the data. The introduced method relies on a recently proposed framework for learning on distributions — by representing them with mean elements in reproducing kernel Hilbert spaces (RKHS) and formulating a classification algorithm therein. In particular, we associate...
Document is unavailable: This DOI was registered to an article that was not presented by the author(s) at this conference. As per section 8.2.1.B.13 of IEEE's "Publication Services and Products Board Operations Manual," IEEE has chosen to exclude this article from distribution. We regret any inconvenience.
Domain adaptation (DA) algorithms address the problem of distribution shift between training and testing data. Recent approaches transform data into a shared subspace by minimizing the shift between their marginal distributions. We propose a method to learn a common subspace that will leverage the class conditional distributions of training samples along with reducing the marginal distribution shift...
We address the problem of how to design a more effective co-training scheme to tackle the multi-view spectral clustering. The conventional co-training procedure treats information from all views equally and often converges to a compromised consensus view that does not fully utilize the multiview information. We instead propose to learn an augmented view and construct its corresponding affinity matrix...
We proposed a novel model to predict human's visual attention when free-viewing webpages. Compared with natural images, webpages are usually full of salient regions such as logos, text, and faces, while few of them attract human's attention in a short sight. Moreover, webpages perform distinct viewing patterns which are quite different from the natural images. In this paper, we introduced multi-features...
In this paper we introduce a novel representation for the classification of 3D images. Unlike most current approaches, our representation is not based on a fixed pyramid but adapts to image content and uses image regions instead of rectangular pyramid scales. Image characteristics, such as depth and color, are used for defining regions within images. Multiple region scales are formed in order to construct...
Image deconvolution is the task to recover the image information that was lost by taking photos with blur motion. Especially blind image deconvolution requires no prior informations other than the blurred image. This problem is seriously ill-posed and an additional operation is required such as extracting image features. In this paper, we present a blind image deconvolution framework using a specified...
The discrete Fourier transform is an important tool for processing digital images. Efficient algorithms for computing the Fourier transform are known as fast Fourier transforms (FFTs). One of the most common of these is the Cooley-Tukey radix-2 decimation algorithm that efficiently transforms one-dimensional data into its frequency domain representation. The orthogonality of rectangular sampling allows...
This paper proposes a classification approach for hyperspectral image (HSI) using the local receptive fields based kernel extreme learning machine. Extreme learning machine (ELM) has drawn increasing attention in the pattern recognition filed due to its simpleness, speediness and good generalization ability. A kernel method is often used to promote ELM's performance, which is known as kernel ELM....
The approximation of nonlinear kernels via linear feature maps has recently gained interest due to their applications in reducing the training and testing time of kernel-based learning algorithms. Current random projection methods avoid the curse of dimensionality by embedding the nonlinear feature space into a low dimensional Euclidean space to create nonlinear kernels. We introduce a Layered Random...
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