基于属性学习的图像分类研究

图像中所蕴含的属性对于图像识别有着重要作用,以往的传统分类方法往往忽略了这些特性,为此,提出一种将稀疏表示和属性学习结合用于图像分类的新方法。该方法首先对图像特征进行稀疏分解,利用系数稀疏表示重构图像特征,然后将重构的特征数据用于属性学习,通过属性分类器的训练学习完成对目标图像的属性识别,达到识别出图像种类的目的。在植物数据集上的对比试验证实了该算法的有效性和在识别准确率上相对于传统识别算法的提升。     

人脸识别中基于系数相似性的字典学习算法

在现有的基于稀疏表示分类算法的人脸识别中,使用通过稀疏学习得到的精简字典可以提高识别速度和精确度。metaface学习(Metaface Learning,MFL)算法在字典学习过程中没有考虑同类样本稀疏编码系数之间具有相似性的特点。为了利用这一信息来提高字典的区分性,提出了一种基于系数相似性的metaface学习(Coefficient-Simi-larity-based Metaface earning,CS-MFL)算法。CS-MFL算法的学习过程中,在更新稀疏表示系数阶段加入同类训练样本稀疏编码系数相似的约束项。为了求解包含系数相似性约束的新的最优化问题,将目标函数中的两个l2范数约束项进行合并,将原问题转化为典型l₂- l1问题进行求解。在不同的人脸库上进行实验,结果表明,提出的CS-MFL算法能够获得比MFL算法更高的识别率,说明由CS-MFL算法学习得到的字典更高效且更具区分性。     

Multitask dictionary learning and sparse representation based single-image super-resolution reconstruction

Recent researches have shown that the sparse representation based technology can lead to state of art super-resolution image reconstruction (SRIR) result. It relies on the idea that the low-resolution (LR) image patches can be regarded as down sampled version of high-resolution (HR) images, whose patches are assumed to have a sparser presentation with respect to a dictionary of prototype patches. In order to avoid a large training patches database and obtain more accurate recovery of HR images, in this paper we introduce the concept of examples-aided redundant dictionary learning into the single-image super-resolution reconstruction, and propose a multiple dictionaries learning scheme inspired by multitask learning. Compact redundant dictionaries are learned from samples classified by K-means clustering in order to provide each sample a more appropriate dictionary for image reconstruction. Compared with the available SRIR methods, the proposed method has the following characteristics: (1) introducing the example patches-aided dictionary learning in the sparse representation based SRIR, in order to reduce the intensive computation complexity brought by enormous dictionary, (2) using the multitask learning and prior from HR image examples to reconstruct similar HR images to obtain better reconstruction result and (3) adopting the offline dictionaries learning and online reconstruction, making a rapid reconstruction possible. Some experiments are taken on testing the proposed method on some natural images, and the results show that a small set of randomly chosen raw patches from training images and small number of atoms can produce good reconstruction result. Both the visual result and the numerical guidelines prove its superiority to some start-of-art SRIR methods.     

Ensemble dictionary learning for saliency detection

The human visual system (HSV) is quite adept at swiftly detecting objects of interest in complex visual scene. Simulating human visual system to detect visually salient regions of an image has been one of the active topics in computer vision. Inspired by random sampling based bagging ensemble learning method, an ensemble dictionary learning (EDL) framework for saliency detection is proposed in this paper. Instead of learning a universal dictionary requiring a large number of training samples to be collected from natural images, multiple over-complete dictionaries are independently learned with a small portion of randomly selected samples from the input image itself, resulting in more flexible multiple sparse representations for each of the image patches. To boost the distinctness of salient patch from background region, we present a reconstruction residual based method for dictionary atom reduction. Meanwhile, with the obtained multiple probabilistic saliency responses for each of the patches, the combination of them is finally carried out from the probabilistic perspective to achieve better predictive performance on saliency region. Experimental results on several open test datasets and some natural images demonstrate that the proposed EDL for saliency detection is much more competitive compared with some existing state-of-the-art algorithms.     

Online dictionary learning algorithm with periodic updates and its application to image denoising

We introduce a coefficient update procedure into existing batch and online dictionary learning algorithms. We first propose an algorithm which is a coefficient updated version of the Method of Optimal Directions (MOD) dictionary learning algorithm (DLA). The MOD algorithm with coefficient updates presents a computationally expensive dictionary learning iteration with high convergence rate. Secondly, we present a periodically coefficient updated version of the online Recursive Least Squares (RLS)-DLA, where the data is used sequentially to gradually improve the learned dictionary. The developed algorithm provides a periodical update improvement over the RLS-DLA, and we call it as the Periodically Updated RLS Estimate (PURE) algorithm for dictionary learning. The performance of the proposed DLAs in synthetic dictionary learning and image denoising settings demonstrates that the coefficient update procedure improves the dictionary learning ability.     

Subsampled terahertz data reconstruction based on spatio-temporal dictionary learning

In this paper, the problem of terahertz pulsed imaging and reconstruction is addressed. It is assumed that an incomplete (subsampled) three dimensional THz data set has been acquired and the aim is to recover all missing samples. A sparsity-inducing approach is proposed for this purpose. First, a simple interpolation is applied to incomplete noisy data. Then, we propose a spatio-temporal dictionary learning method to obtain an appropriate sparse representation of data based on a joint sparse recovery algorithm. Then, using the sparse coefficients and the learned dictionary, the 3D data is effectively denoised by minimizing a simple cost function. We consider two types of terahertz data to evaluate the performance of the proposed approach: THz data acquired for a model sample with clear layered structures (e.g., a T-shape plastic sheet buried in a polythene pellet), and pharmaceutical tablet data (with low spatial resolution). The achieved signal-to-noise-ratio for reconstruction of T-shape data, from only 5% observation was 19 dB. Moreover, the accuracies of obtained thickness and depth measurements for pharmaceutical tablet data after reconstruction from 10% observation were 98.8%, and 99.9%, respectively. These results, along with chemical mapping analysis, presented at the end of this paper, confirm the accuracy of the proposed method.     

基于冗余小波变换与引导滤波的多聚焦图像融合

针对传统多尺度变换在多聚焦图像融合中存在的边缘晕圈问题,提出了一种基于冗余小波变换与引导滤波的多聚焦图像融合算法。首先,利用冗余小波变换对图像进行多尺度分解,将源图像分解为一个相似平面和一系列小波平面,该多尺度分解能够有效地提取源图像中的细节信息;然后,对相似平面和小波平面分别采用引导滤波的加权融合规则来构造加权映射,从而得到相似平面和小波平面的加权融合系数;最后,进行冗余小波逆变换,即可得到融合结果图。实验结果表明,与传统融合算法相比,所提算法能够更好地体现图像边缘的细节特征,取得了较好的融合效果。     

Learning low-rank and discriminative dictionary for image classification

Dictionary learning plays a crucial role in sparse representation based image classification. In this paper, we propose a novel approach to learn a discriminative dictionary with low-rank regularization on the dictionary. Specifically, we apply Fisher discriminant function to the coding coefficients to make the dictionary more discerning, that is, a small ratio of the within-class scatter to between-class scatter. In practice, noisy information in the training samples will undermine the discriminative ability of the dictionary. Inspired by the recent advances in low-rank matrix recovery theory, we apply low-rank regularization on the dictionary to tackle this problem. The iterative projection method (IPM) and inexact augmented Lagrange multiplier (ALM) algorithm are adopted to solve our objective function. The proposed discriminative dictionary learning with low-rank regularization (D²L²R²) approach is evaluated on four face and digit image datasets in comparison with existing representative dictionary learning and classification algorithms. The experimental results demonstrate the superiority of our approach.     

基于非局部自相似性的遥感图像稀疏去噪算法

基于非局部自相似性的遥感图像稀疏去噪方法研究,在为后续的图像分析、识别以及较高层次的处理提供保证方面具有重要意义。针对遥感图像中存在非局部自相似性和稀疏性,在分析传统稀疏去噪模型的基础上,将具有相似结构的非局部块构建成组,用组作为稀疏表示单元,利用基于组正则化稀疏模型进行图像去噪。此外,针对采用整幅图像进行字典学习具有高计算复杂度,分析组特点,为每个组自适应学习一个字典。最后,为获得有效的去噪结果,利用迭代收缩阈值算法解决L0最小化问题。以\"资源三号\"遥感图像为数据进行实验,结果表明,该算法能较好地去除遥感图像的噪声,提高图像的峰值信噪比,保持图像结构信息。基于非局部自相似性的遥感图像稀疏去噪算法能够充分利用图像块信息有效的去除图像中的噪声,提高图像质量。     

Image decomposition via learning the morphological diversity

Image decomposition aims to separate different features in images. Based on dictionary learning (DL) techniques, this letter discusses two new algorithms for image decomposing into a linear combination of morphological components. The proposed algorithms can be viewed as the extensions of DL-based image denoising algorithm. Experiments show that the learned dictionaries by the proposed algorithms can describe the different components of image effectively and leads to high quality image decomposition performance.     

Kernel-based sparse representation for gesture recognition

In this paper, we propose a novel sparse representation based framework for classifying complicated human gestures captured as multi-variate time series (MTS). The novel feature extraction strategy, CovSVDK, can overcome the problem of inconsistent lengths among MTS data and is robust to the large variability within human gestures. Compared with PCA and LDA, the CovSVDK features are more effective in preserving discriminative information and are more efficient to compute over large-scale MTS datasets. In addition, we propose a new approach to kernelize sparse representation. Through kernelization, realized dictionary atoms are more separable for sparse coding algorithms and nonlinear relationships among data are conveniently transformed into linear relationships in the kernel space, which leads to more effective classification. Finally, the superiority of the proposed framework is demonstrated through extensive experiments.     

基于多尺度稀疏表示的图像融合方法

针对目前基于稀疏表示的常用图像融合算法计算复杂度高以及忽略图像局部特征的问题,提出多尺度稀疏表示(multi-scale sparse representation,MSR)的图像融合方法.充分利用小波多尺度分析较好突出图像局部特征的特点,将其和过完备稀疏表示有效结合;待融合图像在小波解析域中进行小波多层分解,对每个尺度的特征运用K-SVD (kernel singular value decomposition)多尺度字典进行OMP (orthogonal matching pursuit)稀疏编码,并在小波域中各个尺度中进行融合.实验结果表明,与传统的小波变换、轮廓波变换、稀疏表示融合算法相比,该算法更能保证图像局部特征的完整性,实现更好的性能.     

基于K-SVD超声渡越时间获取方法研究

在超声回波检测信号中,反映污垢特征的冲击信号非常微弱,容易被噪声淹没。针对信号稀疏分解中常用匹配追踪分解不够准确的问题,提出基于K-SVD奇异值分解的超声渡越时间获取方法,利用K-SVD训练得到超声回波信号的过完备字典,结合正交匹配追踪进行局部搜索适配原子,以提高信号稀疏分解的速度和准确度。基于Comsol Multipysics仿真软件建立充液污垢管道三维有限元模型,研究了超声回波传播特性规律。将K-SVD算法应用于超声回波仿真信号和换热污垢管道回波检测信号的处理,并与原始小波训练字典进行对比。结果表明：改进的K-SVD字典学习算法能够在提高信号稀疏分解的同时,获得较好的降噪结果和污垢特征信息提取,对超声检测信号的处理具有实际意义。     

一种新的结合SVM和FNN的多聚焦图像融合算法

针对基于分块的图像融合中分块裂痕和实际融合特征的不确定等问题,提出一种结合支持向量机(SVM)和模糊神经网络(FNN)的多聚焦图像融合新方法。首先,通过模糊C均值聚类(FCM)和SVM获得FNN的网络参数,利用构建的模糊神经网络,将分割的图像块分成清晰区域、模糊区域和过渡区域三类;然后用模糊神经网络的反模糊化输出作为权值因子对三类区域进行加权融合,输出融合的多聚焦图像。最后,通过均方根误差、平均绝对误差和峰值信噪比等指标对多种融合算法进行融合质量评价。实验结果表明,提出的融合算法鲁棒性和计算性能较好,基本满足实际图像融合的需求,且融合质量评价也表明本文方法优于现有的融合算法。     

基于改进的字典学习算法的图像去噪方法

提出对基于MOD和K-SVD字典学习算法的图像去噪的两个方面的改进。在字典更新阶段,采用一种新的字典更新方式,在保持支集完备的同时寻找字典和表示法。在稀疏编码阶段,根据前一次追踪过程产生的部分系数进行修正和更新。分别对这两种改进进行了验证,并说明了如何进行更快速的训练以及取得更好的结果,实验结果证实了论文方法的有效性。     

基于K-PSO稀疏表示的故障分类方法研究

针对现代复杂生产过程中不能准确识别、分类多种故障的问题,提出一种改进的稀疏表示故障分类方法。该方法依据信号的稀疏表示来判断故障所属类别。其具体实现过程首先是利用K-均值奇异值分解(K-SVD)算法构造过完备字典,使其包含原信息的主要特征,再通过粒子群(PSO)算法有效地搜索并寻找稀疏分解中产生的在过完备字典范围中的最匹配原子,最后利用以该匹配原子为基础的稀疏表示结果实现对多故障问题的分类识别。运用数值仿真验证了该算法的可行性和有效性。同时,针对柴油机燃油系统的故障分类,将该方法与基于BP神经网络和SVM的分类识别方法进行比较,实验表明该算法在故障分类上具有更好的效果。     

Integration of multi-feature fusion and dictionary learning for face recognition

Recent research emphasizes more on analyzing multiple features to improve face recognition (FR) performance. One popular scheme is to extend the sparse representation based classification framework with various sparse constraints. Although these methods jointly study multiple features through the constraints, they just process each feature individually such that they overlook the possible high-level relationship among different features. It is reasonable to assume that the low-level features of facial images, such as edge information and smoothed/low-frequency image, can be fused into a more compact and more discriminative representation based on the latent high-level relationship. FR on the fused features is anticipated to produce better performance than that on the original features, since they provide more favorable properties. Focusing on this, we propose two different strategies which start from fusing multiple features and then exploit the dictionary learning (DL) framework for better FR performance. The first strategy is a simple and efficient two-step model, which learns a fusion matrix from training face images to fuse multiple features and then learns class-specific dictionaries based on the fused features. The second one is a more effective model requiring more computational time that learns the fusion matrix and the class-specific dictionaries simultaneously within an iterative optimization procedure. Besides, the second model considers to separate the shared common components from class-specified dictionaries to enhance the discrimination power of the dictionaries. The proposed strategies, which integrate multi-feature fusion process and dictionary learning framework for FR, realize the following goals: (1) exploiting multiple features of face images for better FR performances; (2) learning a fusion matrix to merge the features into a more compact and more discriminative representation; (3) learning class-specific dictionaries with consideration of the common patterns for better classification performance. We perform a series of experiments on public available databases to evaluate our methods, and the experimental results demonstrate the effectiveness of the proposed models.