Camera constraint-free view-based 3-D object retrieval

Recently, extensive research efforts have been dedicated to view-based methods for 3-D object retrieval due to the highly discriminative property of multiviews for 3-D object representation. However, most of state-of-the-art approaches highly depend on their own camera array settings for capturing views of 3-D objects. In order to move toward a general framework for 3-D object retrieval without the limitation of camera array restriction, a camera constraint-free view-based (CCFV) 3-D object retrieval algorithm is proposed in this paper. In this framework, each object is represented by a free set of views, which means that these views can be captured from any direction without camera constraint. For each query object, we first cluster all query views to generate the view clusters, which are then used to build the query models. For a more accurate 3-D object comparison, a positive matching model and a negative matching model are individually trained using positive and negative matched samples, respectively. The CCFV model is generated on the basis of the query Gaussian models by combining the positive matching model and the negative matching model. The CCFV removes the constraint of static camera array settings for view capturing and can be applied to any view-based 3-D object database. We conduct experiments on the National Taiwan University 3-D model database and the ETH 3-D object database. Experimental results show that the proposed scheme can achieve better performance than state-of-the-art methods.     

3D model retrieval using weighted bipartite graph matching

In this paper, we propose a view-based 3D model retrieval algorithm, where many-to-many matching method, weighted bipartite graph matching, is employed for comparison between two 3D models. In this work, each 3D model is represented by a set of 2D views. Representative views are first selected from the query model and the corresponding initial weights are provided. These initial weights are further updated based on the relationship among these representative views. The weighted bipartite graph is built with these selected 2D views, and the matching result is used to measure the similarity between two 3D models. Experimental results and comparison with existing methods show the effectiveness of the proposed algorithm.     

Collaborative object tracking model with local sparse representation

There existed many visual tracking methods that are based on sparse representation model, most of them were either generative or discriminative, which made object tracking more difficult when objects have undergone large pose change, illumination variation or partial occlusion. To address this issue, in this paper we propose a collaborative object tracking model with local sparse representation. The key idea of our method is to develop a local sparse representation-based discriminative model (SRDM) and a local sparse representation-based generative model (SRGM). In the SRDM module, the appearance of a target is modeled by local sparse codes that can be formed as training data for a linear classifier to discriminate the target from the background. In the SRGM module, the appearance of the target is represented by sparse coding histogram and a sparse coding-based similarity measure is applied to compute the distance between histograms of a target candidate and the target template. Finally, a collaborative similarity measure is proposed for measuring the difference of the two models, and then the corresponding likelihood of the target candidates is input into a particle filter framework to estimate the target state sequentially over time in visual tracking. Experiments on some publicly available benchmarks of video sequences showed that our proposed tracker is robust and effective.     

一种采用模型基辅助的混合视频编码方法

本文提出一种帧内采用波形编码，帧间采用模型基编码的混合视频压缩系统，增强了模型基编码的适用性，改进了CANDIDE头部模型，使之易于匹配，提高了压缩编码的效率，采用仿射率法取代了蒙皮法，克服了遮挡问题，提高了合成图象的主观质量和运算速度。     

基于卷积神经网络的高效三维模型检索方法

目前基于视图的三维模型检索已经成为一个研究热点.该方法首先将三维模型表示为二维视图的集合,然后采用深度学习技术进行分类和检索.但是现有的方法在精度和效率方面都有待提升.本文提出了一种新的三维模型检索方法,该方法包括索引建立和模型检索.在索引建立阶段,选择代表性视图输入到训练好的卷积神经网络(Convolutional ...     

Structural sparse coding seeds–active appearance model for object tracking

In this paper, we propose a tracking algorithm that can robustly handle appearance variations in tracking process. Our method is based on seeds–active appearance model, which is composed by structural sparse coding. In order to compensate for illumination changes, heavy occlusion and appearance self-updating problem, we proposed a mixture online learning scheme for modeling the target object appearance model. The proposed object tracking scheme involves three stages: training, detection and tracking. In the training stage, an incremental SVM model that directly measures the candidates samples and target difference. The proposed mixture generate–discriminative method can well separate two highly correlated positive candidates images. In the detection stage, the trained weighted vector is used to separate the target object in positive candidates images with respect to the seeds images. In the tracking stage, we employ the particle filter to track the object through an appearance adaptive updating algorithm with seeds–active constrained sparse representation. Based on a set of comprehensive experiments, our algorithm has demonstrated better performance than alternatives reported in the current literature.     

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。     

Fast wavelet-based image characterization for highly adaptive image retrieval

Adaptive wavelet-based image characterizations have been proposed in previous works for content-based image retrieval (CBIR) applications. In these applications, the same wavelet basis was used to characterize each query image: This wavelet basis was tuned to maximize the retrieval performance in a training data set. We take it one step further in this paper: A different wavelet basis is used to characterize each query image. A regression function, which is tuned to maximize the retrieval performance in the training data set, is used to estimate the best wavelet filter, i.e., in terms of expected retrieval performance, for each query image. A simple image characterization, which is based on the standardized moments of the wavelet coefficient distributions, is presented. An algorithm is proposed to compute this image characterization almost instantly for every possible separable or nonseparable wavelet filter. Therefore, using a different wavelet basis for each query image does not considerably increase computation times. On the other hand, significant retrieval performance increases were obtained in a medical image data set, a texture data set, a face recognition data set, and an object picture data set. This additional flexibility in wavelet adaptation paves the way to relevance feedback on image characterization itself and not simply on the way image characterizations are combined.     

基于多核稀疏编码的三维人体姿态估计

为了准确有效的重构多视角图像中的三维人体姿态,该文提出一种基于多核稀疏编码的人体姿态估计算法.首先,针对连续帧姿态估计的歧义问题,该文设计了一种用于表达多视角图像的HA-SIFT描述子,其中,人体局部拓扑、肢体相对位置及外观信息被同时编码;然后,在多核学习框架下建立同时考虑特征空间内在流形结构与姿态空间几何信息的目标函数,并在希尔伯特空间优化目标函数以更新稀疏编码、过完备字典与多核权值;最后,利用姿态字典原子的线性组合来估计对应未知输入的三维人体姿态.实验结果表明,与核稀疏编码、Laplace稀疏编码及Bayesian稀疏编码相比,文本方法具有更高的估计精度.     

Statistical modeling and many-to-many matching for view-based 3D object retrieval

We address the task of view-based 3D object retrieval, in which each object is represented by a set of views taken from different positions, rather than a geometrical model based on polygonal meshes. As the number of views and the view point setting cannot always be the same for different objects, the retrieval task is more challenging and the existing methods for 3D model retrieval are infeasible. In this paper, the information in the sets of views is exploited from two aspects. On the one hand, the form of histogram is converted from vector to state sequence, and Markov chain (MC) is utilized for modeling the statistical characteristics of all the views representing the same object. On the other hand, the earth mover's distance (EMD) is involved to achieve many-to-many matching between two sets of views. For 3D object retrieval, by combining the above two aspects together, a new distance measure is defined, and a novel approach to automatically determine the edge weights in graph-based semi-supervised learning is proposed. Experimental results on different databases demonstrate the effectiveness of our proposal.     

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。     

基于统计机器翻译模型的查询扩展

在搜索引擎等实际的信息检索应用中,用户提交的查询请求通常都只包含很少的几个关键词,这会引起相关文档与用户查询之间的词不匹配问题,对检索性能有较严重的负面影响。该文在分析了查询产生模型的基础上,提出了一种新的基于统计机器翻译的查询扩展方法。通过统计机器翻译模型提取文档集中与查询词相关联的词,用以进行查询扩展。在TREC数据集上的试验结果表明:基于统计翻译的查询扩展方法不仅比不扩展的语言模型方法始终有12%～17%的提高,而且比流行的查询扩展方法-伪反馈也具有可比的平均准确率。     

n-words模型下Hesse稀疏表示的图像检索算法

论文针对视觉词袋(BOVW)模型放弃图像空间结构的缺点,提出一种基于Hesse稀疏编码的图像检索算法。首先,建立n-words模型,获得图像局部特征表示。n-words模型由一系列连续视觉词获得,是图像特征的一种高级描述。该文从n=1到n=5进行试验,寻找最恰当的n值;其次,将二阶Hesse能量函数融入标准稀疏编码的目标函数,得到Hesse稀疏编码公式;最后,以获得的n-words序列作为编码特征,利用特征符号搜索算法求解最优Hesse系数,计算相似度,返回检索结果。实验在两类数据集上进行,与BOVW模型和已有的算法相比,新算法极大地提高了图像检索的准确率。     

3D model retrieval by sample based alignment

In 3D model retrieval, preprocessing of 3D models is needed, in which alignment is a key factor that significantly affects retrieval performance. In particular, the anti-rotation image feature can obtain the alignment effect of 3D model views. In practice, the focus of many users of 3D models is not just on retrieval performance, but the use of aligned models for different purposes. In this paper, we propose a method, namely Sample Based Alignment (SBA) for better 3D model alignment and retrieval. In SBA, given a class, a sample model is used as the target for alignment, after which each 3D model in this class is then aligned one by one, i.e., the 3D model is actually rotated. Our experimental results, based on two 3D model datasets and performance comparisons with other methods, demonstrate the superiority of the SBA method over state-of-the-art methods in terms of 3D model retrieval and classification.     

有监督低秩子空间恢复的正则鲁棒稀疏表示人脸识别算法

针对训练样本和测试样本均存在光照及遮挡时,破坏图像低秩结构问题,本文提出基于监督低秩子空间恢复的正则鲁棒稀疏表示人脸识别算法。首先,将所有训练样本构造成矩阵D,对矩阵D进行监督低秩矩阵分解,分解为低秩类相关结构A,低秩类内差异结构B和稀疏误差结构E;然后用主成分分析方法找到类相关结构A低秩子空间的变换矩阵;再通过变换矩阵将训练样本和测试样本投影到低秩子空间;最后,在低秩子空间中,通过正则鲁棒稀疏编码进行加权分类识别。在AR和Extended Yale B公开人脸数据库上的实验结果验证本文算法的有效性及鲁棒性。      

运动目标检测的l0群稀疏RPCA模型及其算法

经典的鲁棒主成分分析(Robust Principal Component Analysis,RPCA)目标检测算法使用l₁范数逐一判别每一像素点是否属于运动目标,未能考虑到运动目标在空间分布的连续性,不利于提升运动目标检测的鲁棒性.本文提出了一种基于l₀群稀疏RPCA模型的运动目标检测方法.首先运用Ncuts算法进行区域过分割,生成多个同性区域,将其作为群稀疏约束的分组信息;第二步构造基于l₀群稀疏RPCA模型,运用群稀疏准则判别过分割后的各同性区域是否为运动目标,采用交替方向乘子算法对模型进行快速求解,约束过分割形成的同性区域具有相同检测结果,进而将背景环境和运动前景分离,能够更加准确地度量运动目标的区域边界,且对复杂的背景扰动更加鲁棒,达到了运动目标鲁棒检测的目的.     

基于随机化视觉词典组和上下文语义信息的目标检索方法

 传统的视觉词典法(Bag of Visual Words,BoVW)具有时间效率低、内存消耗大以及视觉单词同义性和歧义性的问题,且当目标区域所包含的信息不能正确或不足以表达用户检索意图时就得不到理想的检索结果.针对这些问题,本文提出了基于随机化视觉词典组和上下文语义信息的目标检索方法.首先,该方法采用精确欧氏位置敏感哈希(Exact Euclidean Locality Sensitive Hashing,E²LSH)对局部特征点进行聚类,生成一组支持动态扩充的随机化视觉词典组;然后,利用查询目标及其周围的视觉单元构造包含上下文语义信息的目标模型;最后,引入K-L散度(Kullback-Leibler divergence)进行相似性度量完成目标检索.实验结果表明,新方法较好地提高了目标对象的可区分性,有效地提高了检索性能.