基于视觉词袋模型的图像分类改进方法

文中主要基于视觉词袋（BOVW, Bag-Of-Visual-Words）模型对图像进行分类处理,并对传统视觉词袋模型存在的不足进行了改进,提出了一种基于视觉词典的权重直方图来表达图像,采用优化的k-means聚类算法（k-means+）用于视觉词典的构建,代入KNN（K-Nearest-Neighbors）分类器进行分类。通过对Caltech 101和Caltech 256这两个经典数据库进行实验,实验结果表明该改进方案较传统方法提高了分类的正确率。     

图像多角度局部特征提取及相似性匹配技术研究

图像特征提取是计算机视觉应用的根本基础.研究了SIFT、LBP和HOG等3种信息互补的局部特征(即多角度局部特征)提取算法,研究了基于稀疏编码的图像相似性匹配算法,并以基于内容的图像检索(CBIR)为应用实例,验证了算法的有效性和高效性.     

基于稀疏自编码的无监督哈希算法

最近邻搜索在大规模图像检索中变得越来越重要。在最近邻搜索中,许多哈希方法因为快速查询和低内存被提出。然而,现有方法在哈希函数构造过程中对数据稀疏结构研究的不足,本文提出了一种无监督的稀疏自编码的图像哈希方法。基于稀疏自编码的图像哈希方法将稀疏构造过程引入哈希函数的学习过程中,即通过利用稀疏自编码器的KL距离对哈希码进行稀疏约束以增强局部保持映射过程中的判别性,同时利用L2范数来哈希编码的量化误差。实验中用两个公共图像检索数据集CIFAR-10和YouTube Faces验证了本文算法相比其他无监督哈希算法的优越性。     

基于卷积稀疏表示的红外与可见光图像融合

稀疏表示是以块为单位进行编码的,因此破坏了图像块间的相关性。针对上述问题,提出了基于卷积稀疏表示的红外与可见光图像融合算法。该算法采用交替方向乘子算法(ADMM)求解非下采样轮廓波变换(NSCT)域强边缘子带的卷积稀疏系数,完成特征响应系数的融合。同时,采用脉冲耦合神经网络(PCNN)模型的点火图完成NSCT域高频子带的融合。实验结果表明:该算法解决了稀疏表示的“块效应”问题,同时又兼具PCNN模型的视觉特性,可以有效地捕捉源图像的特征信息。另外,在主观视觉评价和客观质量评价方面均优于现有算法。     

基于非负稀疏编码的图像检索及应用

针对图像理解中所需的图像检索,提出了一种新的图像检索方法。该方法将非负稀疏编码引入到ScSPM算法中进行图像的特征提取和表示,计算特征表示后图像之间的欧氏距离并排序。实验结果表明该方法在图像理解中能够有效地检索相关图像。     

基于卷积稀疏自编码的图像超分辨率重建

针对卷积稀疏编码算法中特征映射的准确性的问题,为了进一步提高图像超分辨率重建的的质量,文中提出一种基于卷积稀疏自编码的图像超分辨率重建算法。该算法首先在预训练阶段利用稀疏自编码器对输入高低分辨率图像分别进行训练,得到对应的图像稀疏特征表示;然后再由卷积神经网络根据得到的稀疏系数共同训练相应的滤波器及特征映射函数并更新到最优解;最后由高分辨率滤波器和对应的稀疏表示系数卷积求和,得到高分辨率重建图像估计。实验结果显示,改进算法的峰值信噪比（PSNR）结果较卷积稀疏编码算法提高了近0.1 dB,有效提高了重建图像的质量。     

基于异质局部特征的图像稀疏表示

为了有效描述图像的多角度视觉内容,提出一种将图像异质局部特征集通过稀疏学习映射为图像全局稀疏表示的新方法.该方法从不同的训练特征集中学习超完备视觉词典,经过局部稀疏编码、最大值合并、加权联接及归一化等一系列处理步骤融合多种局部特征的互补信息,最终形成一个高维稀疏向量来描述图像的多角度视觉内容.将其应用于基于内容的图像检索(CBIR)任务中,实验结果表明,这种基于异质局部特征学习而来的图像全局稀疏表示解决了单一局部特征集描述图像的局限性和高维局部特征集相似性度量时空复杂度高的问题.     

深度学习和迭代量化在图像检索中的应用研究

基于内容的图像检索的关键在于对图像进行特征提取和对特征进行多比特量化编码 。近年来,基于内容的图像检索使用低级可视化特征对图像进行描述,存在“语义鸿沟”问题;其次,传统量化编码使用随机生成的投影矩阵,该矩阵与特征数据无关,因此不能保证量化的精确度。针对目前存在的这些问题,本文结合深度学习思想与迭代量化思想,提出基于卷积神经网络VGG16和迭代量化(Iterative Quantization, ITQ)的图像检索方法。使用在公开数据集上预训练VGG16网络模型,提取基于深度学习的图像特征;使用ITQ方法对哈希哈函数进行训练,不断逼近特征与设定比特数的哈希码之间的量化误差最小值,实现量化误差的最小化;最后使用获得的哈希码进行图像检索。本文使用查全率、查准率和平均精度均值作为检索效果的评价指标,在Caltech256图像库上进行测试。实验结果表明,本文提出的算法在检索优于其他主流图像检索算法。      

基于词汇树层次语义模型的图像检索算法

解决语义鸿沟必须建立图像低层特征到高层语义的映射,针对此问题,本文提出了一种基于词汇树层次语义模型的图像检索方法.首先提取图像包含颜色信息的SIFT特征来构造图像库的特征词汇树,生成描述图像视觉信息的视觉词汇.并在此基础上利用Bayesian决策理论实现视觉词汇到语义主题信息的映射,进而构造了一个层次语义模型,并在此模型基础上完成了基于内容的语义图像检索算法.通过检索过程中用户的相关反馈,不仅可以加入正反馈图像扩展图像查询库,同时能够修正高层语义映射.实验结果表明,基于该模型的图像检索算法性能稳定,并且随着反馈次数的增加,检索效果明显提升.     

基于分层匹配追踪的快速图像检索

为从海量的图像资源中既准确又快速地检索出目 标图像,在传统的 图像检索模型中,图像的特征通常是从固定尺度的图像上提取出的,这将不可避免地降低整 个系统 实际应用能力。为解决这一问题,本文引入分层稀疏编码模型, 提出一种基于分层匹配追踪(HMP)的快速图像检索技术,实现多尺度情况下的图像检索。本 文方法从图像中提取的低层稀疏编码特征传递 到高层,并将提取的高维稀疏编码特征转换为改进后的PCAH特征,利用哈希特征的汉明距 离度量, 实现图像的快速检索。在公共数据集Caltech256和Corel5K上的实验 结果可以看出,本文方法的查 准率和查全率较其他哈希法分别提高了5%和10%以上,而且所用时间 也最短,表明本文方法不仅具有较高的准确率,还能保持较高的时间效率。     

Single-Image Super-Resolution Reconstruction via Learned Geometric Dictionaries and Clustered Sparse Coding

Recently, single image super-resolution reconstruction (SISR) via sparse coding has attracted increasing interest. In this paper, we proposed a multiple-geometric-dictionaries-based clustered sparse coding scheme for SISR. Firstly, a large number of high-resolution (HR) image patches are randomly extracted from a set of example training images and clustered into several groups of "geometric patches," from which the corresponding "geometric dictionaries" are learned to further sparsely code each local patch in a low-resolution image. A clustering aggregation is performed on the HR patches recovered by different dictionaries, followed by a subsequent patch aggregation to estimate the HR image. Considering that there are often many repetitive image structures in an image, we add a self-similarity constraint on the recovered image in patch aggregation to reveal new features and details. Finally, the HR residual image is estimated by the proposed recovery method and compensated to better preserve the subtle details of the images. Some experiments test the proposed method on natural images, and the results show that the proposed method outperforms its counterparts in both visual fidelity and numerical measures.     

局部非负稀疏编码的高光谱目标检测方法研究

基于稀疏编码的高光谱图像处理算法能够挖掘高光谱高维数据空间中潜在的数据相关性,能自然地贴近光谱信号的本质特征。本文提出基于非负稀疏编码的高光谱目标检测算法。与经典稀疏编码模型相比,非负稀疏编码对编码系数进行非负约束,一方面使得线性编码具有明确的物理解释,另一方面增强了系数的可分性与稳健性。算法首先通过双窗口设计构造局部动态字典,然后利用目标和背景在动态字典上编码的稀疏性差异进行阈值分割最后通过统计判决实现目标检测。仿真数据以及真实数据实验结果证明了算法的有效性。      

3D object retrieval based on sparse coding in weak supervision

With the rapid development of computer vision and digital capture equipment, we can easily record the 3D information of objects. In the recent years, more and more 3D data are generated, which makes it desirable to develop effective 3D retrieval algorithms. In this paper, we apply the sparse coding method in a weakly supervision manner to address 3D model retrieval. First, each 3D object, which is represented by a set of 2D images, is used to learn dictionary. Then, sparse coding is used to compute the reconstruction residual for each query object. Finally, the residual between the query model and the candidate model is used for 3D model retrieval. In the experiment, ETH, NTU and ALOL dataset are used to evaluate the performance of the proposed method. The results demonstrate the superiority of the proposed method.     

一种改进的基于K-SVD字典的图像去噪算法

为了更好地实现图像的去噪效果,提出了一种改进的基于K-SVD（Singular Value Decomposition）字典学习的图像去噪算法。首先,将输入的含噪信号进行K均值聚类分解,将得到的图像块进行稀疏贝叶斯学习和噪声的更新,当迭代到一定次数时继续使用正交匹配追踪（Orthogonal Matching Pursuit,OMP）算法对图像块进行稀疏编码,然后在完成稀疏编码的基础上通过奇异值分解来逐列更新字典,反复迭代至得到过完备字典以实现稀疏表示,最后对处理过的图像进行重构,得到去噪后的图像。实验结果表明,本文的改进算法相对于传统的K-SVD字典的图像去噪能够在保留图像边缘和细节信息的同时,更有效地去除图像中的噪声,具有更好的视觉效果。     

A new method for image retrieval based on analyzing fractal coding characters

It has been effectively proved that histogram of image fractal coding parameters can be used for image retrieval. In recent years, many researchers have paid more and more attention to this application of image fractal coding. In this paper, a new statistical method, based on kernel density estimation, is used for analyzing fractal coding parameters. The fractal signatures are then extracted for texture image retrieval. Experimental results show that the proposed method not only has higher retrieval rate but also faster retrieval speed than existing method.     

Second-order optimization of mutual information for real-time image registration

In this paper, we present a direct image registration approach that uses mutual information (MI) as a metric for alignment. The proposed approach is robust and gives an accurate estimation of a set of 2-D motion parameters in real time. MI is a measure of the quantity of information shared by signals. Although it has the ability to perform robust alignment with illumination changes, multimodality, and partial occlusions, few works have proposed MI-based applications related to spatiotemporal image registration or object tracking in image sequences because of some optimization problems, which we will explain. In this paper, we propose a new optimization method that is adapted to the MI cost function and gives a practical solution for real-time tracking. We show that by refining the computation of the Hessian matrix and using a specific optimization approach, the registration results are far more robust and accurate than the existing solutions, with the computation also being cheaper. A new approach is also proposed to speed up the computation of the derivatives and keep the same optimization efficiency. To validate the advantages of the proposed approach, several experiments are performed.     

基于二维压缩感知和分层特征的图像检索算法

为了保留图像分析时的像素点位置关系及降维处理,把一维压缩感知理论推广到二维,建立了二维可稀疏信号的压缩测量模型,研究了一种二维信号的自适应梯度下降重构AGDR(Adaptive Gradient Descent Recursion)算法,由此提出了一种图像分层特征提取与检索方法.首先对图像在RGB颜色空间上进行网格离散划分,通过分层算子对图像进行分层映射,定义一种基于颜色网格空间的扩展灰度共生矩阵,采用二维测量模型获取图像的分层测量特征、纹理特征与分层颜色统计特征,图像分层测量特征综合反映出图像的颜色及像素点位置的关系,扩展灰度共生矩阵反映纹理特征.其次用AGDR算法计算检索图像之间的原始信号差量及其稀疏值.最后结合两类分层特征差量、稀疏值和颜色统计特征,融合计算图像间整体相似度度量指标.仿真实验表明,应用分层二维压缩感知测量与AGDR算法的图像检索方法在检索时间、查全率和查准率等指标上具有优越性能,为图像检索提供了新思路.     

Compression of facial images using the K-SVD algorithm

The use of sparse representations in signal and image processing is gradually increasing in the past several years. Obtaining an overcomplete dictionary from a set of signals allows us to represent them as a sparse linear combination of dictionary atoms. Pursuit algorithms are then used for signal decomposition. A recent work introduced the K-SVD algorithm, which is a novel method for training overcomplete dictionaries that lead to sparse signal representation. In this work we propose a new method for compressing facial images, based on the K-SVD algorithm. We train K-SVD dictionaries for predefined image patches, and compress each new image according to these dictionaries. The encoding is based on sparse coding of each image patch using the relevant trained dictionary, and the decoding is a simple reconstruction of the patches by linear combination of atoms. An essential pre-process stage for this method is an image alignment procedure, where several facial features are detected and geometrically warped into a canonical spatial location. We present this new method, analyze its results and compare it to several competing compression techniques.