一种基于混合特征核的图像检索方法

为了更准确地描述图像的视觉特征,提高图像检索的查准率与查全率,提出了一种基于混合特征核的图像检索方法.该方法提取图像的颜色、纹理、SIFT特征,引入高斯核函数,建立图像的混合特征核模型,在高维的核空间进行基于核的图像聚类.实验表明,该混合模型与传统多特征融合方法以及单一特征核方法相比,能够更好地表示图像的视觉特征,提高检索的查准率和查全率.     

多模型融合的多标签图像自动标注

为了实现更为准确的复杂语义内容图像理解,提出一种融合多模型的多标签图像自动标注方法.该方法采用3个不同的模型分别对图像语义内容进行分析:在前景语义概念检测中,提出一种基于多特征的视觉显著性分析方法,并利用多Nystrm近似核对前景对象的语义进行判别分析;对于背景概念检测,提出一种区域语义分析的方法;通过构造基于潜语义分析的语义相关矩阵来消除标注错误的标签.根据前景和背景的语义和视觉特征,分别采用不同的模型提取前景和背景标注词,而语义相关分析能够有效地提高标注的准确性.实验结果表明,该多模型融合标注方法在图像的深层语义分析以及多标签标注方面具有较好的效果;与同类算法相比,能够有效地减少错误标注的标签数目,得到更加准确的标注结果.     

用KPCA-SVM的方法检测垃圾标签的研究

高维数据中进行各种处理时所需样本数量会成指数级增加,同时样本间距离的价值也逐渐减小,将导致维数灾问题。文本标签数据通常会面临数据维数过高的问题,会影响用户对垃圾标签的检测。文中借助支持向量机的数学模型构建出针对Folksonomy的大规模垃圾标签检测模型。为了减少检测垃圾标签时维数过高的影响,在核主成分分析理论的启发下,将数据降维思想引入数据约简领域,提出基于核主成分分析法的大规模SVM数据集约简模型。最终实例化形成一种新的垃圾标签检测方法,即基于核主成分分析支持向量机（ KPCA-SVM）的大规模垃圾标签检测模型。该模型在垃圾标签检测中可以在不影响数据特征的前提下,缩短模型的测试时间且检测性能良好。     

基于用户注意力与视觉注意力的社交图像描述

图像描述是机器学习和计算机视觉的重要研究领域,但现有方法对于视觉特征和模型架构之间存在的语义信息关联性探索还存在不足.本文提出了一种基于用户标签、视觉特征的注意力模型架构,能够有效地结合社交图像特征和图像中用户标签生成更加准确的描述.我们在MSCOCO数据集上进行了实验来验证算法性能,实验结果表明本文提出的基于用户标签、视觉特征的注意力模型与传统方法相比具有明显的优越性.     

基于核密度估计的图像自动标注方法

提出一种基于概率模型的图像自动语义标注方法,将图片自动标注看作一个多类分类问题,通过无参数的核密度估计,实现用含有共同标注词的图片组估计视觉特征和相应标注词之间关系的机制。选取表达能力较好的基于CPAM的视觉特征,无须对图像进行语义分割处理,有效提高核密度估计的效率。在基准数据集上进行实验,结果表明,该模型能够获得比当前其他相关方法更好的标注性能。     

基于低秩稀疏分解优化的图像标签完备

大量上传的网络图像因用户语义标注的随意性,造成了图像标签的不完备,大大降低了图像检索的效率.低秩稀疏是一种有效降低数据噪声的方法.为提高图像语义标签完备的准确度,提出一种基于低秩稀疏分解优化(LRSDO)的图像标签完备方法.首先结合待完备图像的视觉特征和语义搜索其近邻图像集;然后通过低秩稀疏分解模型获得其视觉特征与语义之间的映射关系,并以此预测该图像的候选标签;最后使用面向个体的标签共现频率方法对候选标签进行去噪优化,进而实现对其更加准确的自动图像标签完备.在基准数据集Corel5K和真实数据集Flickr30Concepts上进行了实验,结果表明,该方法在图像标签完备的平均准确率,平均召回率和覆盖率上均表现出更优的性能.     

基于MPEG-7和MM混合模型的图像自动标注算法

为了弥补图像低层视觉特征和高层语义之间的"语义鸿沟",改善图像自动标注的性能,提出了基于多媒体描述接口(MPEG-7)和MM(Mixture Model)混合模型的图像标注算法。该算法采用MPEG-7标准推荐的颜色和纹理描述子提取图像的低层视觉特征,通过MM混合模型建立低层特征到高层语义空间的映射,实现了基于图像整体低层特征的多标签图像自动标注。通过在corel图像数据集上的一系列实验测试验证了该方法的可行性和有效性。     

大规模图像语义相关性自动标注

针对大规模图像集合的自动标注问题,提出一种图像语义相关性自动标注方法.首先提取图像的视觉特征,将每个样本表示为局部邻域样本点的稀疏线性组合;然后采用一种基于最大后验概率准则的多标签学习方法得到每幅图像的单特征标签相关度;最终对单个特征和特定标签的相关度阈值进行无偏估计,并采用无监督组合方法融合多种视觉特征和标签的相关度.互联网数据集测试结果表明,该方法是有效的.     

基于连续预测的半监督学习图像语义标注

为了在图像底层特征与高层语义之间建立关系,提高图像自动标注的精确度,结合基于图学习的方法和基于分类的标注算法,提出了基于连续预测的半监督学习图像语义标注的方法,并对该方法的复杂度进行分析。该方法利用标签数据提供的信息和标签事例与无标签事例之间的关系,根据邻接点(事例)属于同一个类的事实,构建K邻近图。用一个基于图的分类器,通过核函数有效地计算邻接信息。在建立图的基础上,把经过划分后的样本节点集通过基于连续预测的多标签半监督学习方法进行标签传递。实验表明,提出的算法在图像标注中的标注词的平均查准率、平均查全率方面有显著的提高。     

基于视觉和语义互学习的标签完备标注

大多数图像标签完备方法仅依据待完备图像的视觉特征搜索近邻图像,获取候选标签实现完备,往往忽略候选标签与初始标签隐含的语义和视觉关系.为有效完备图像标签,本文依据信息的相互学习机制提出一种基于视觉和语义互学习的标签完备标注方法.首先,依据待完备图像的视觉特征和初始标签语义,搜索其近邻图像集并获取候选标签;其次,根据候选标...     

基于图文混排的传统服饰图像以文标图算法

针对高效解读和智能处理海量图文资料是一项极具挑战并具有实用价值工作,而自动标注精度又面临依赖训练样本的难题,提出了一种基于数字图文混排书籍以文标图方法,由混排版式识别预处理、领域图像语义标签构建和大标签空间以文标图算法3部分组成.首先,通过提出的混排版式识别离算法,提取数字图文混排版式中图像、标题及描述文本等内容.然后...     

Learning Social Tag Relevance by Neighbor Voting

Social image analysis and retrieval is important for helping people organize and access the increasing amount of user tagged multimedia. Since user tagging is known to be uncontrolled, ambiguous, and overly personalized, a fundamental problem is how to interpret the relevance of a user-contributed tag with respect to the visual content the tag is describing. Intuitively, if different persons label visually similar images using the same tags, these tags are likely to reflect objective aspects of the visual content. Starting from this intuition, we propose in this paper a neighbor voting algorithm which accurately and efficiently learns tag relevance by accumulating votes from visual neighbors. Under a set of well-defined and realistic assumptions, we prove that our algorithm is a good tag relevance measurement for both image ranking and tag ranking. Three experiments on 3.5 million Flickr photos demonstrate the general applicability of our algorithm in both social image retrieval and image tag suggestion. Our tag relevance learning algorithm substantially improves upon baselines for all the experiments. The results suggest that the proposed algorithm is promising for real-world applications.     

丰富图像标签的正则化非负矩阵分解方法

目的 随着Web2.0技术的进步,以用户生成内容为中心的社交网站蓬勃发展,也使得基于图像标签的图像检索技术越来越重要。但是,由于用户标注时的随意性和个性化,导致用户提交的图像标签不够完备,降低了图像检索的准确性。方法 针对这一问题,提出一种正则化的非负矩阵分解方法来丰富图像欠完备的标签,提高图像标签的完备性。利用非负矩阵分解的方法将原始的标签-图像矩阵投影到潜在的低秩空间里消除噪声,同时利用图像的类内视觉离散度作为正则化项提高消除噪声、丰富标签的效果。结果 利用从社交网站Flickr上下载的大量社交图像进行对比实验,验证了本文方法对丰富图像标签的有效性。通过对比目前流行的优化算法,本文算法获得较高的性能提升,算法平均准确度提高了12.3%。结论 将图像类内视觉离散度作为正则化项的非负矩阵分解算法,能较好地丰富社交图像的标签,解决网络图像标签的欠完备问题。     

融合深度特征和语义邻域的自动图像标注*

传统图像标注方法中人工选取特征费时费力,传统标签传播算法忽视语义近邻,导致视觉相似而语义不相似,影响标注效果.针对上述问题,文中提出融合深度特征和语义邻域的自动图像标注方法.首先构建基于深度卷积神经网络的统一、自适应深度特征提取框架,然后对训练集划分语义组并建立待标注图像的邻域图像集,最后根据视觉距离计算邻域图像各标签的贡献值并排序得到标注关键词.在基准数据集上实验表明,相比传统人工综合特征,文中提出的深度特征维数更低,效果更好.文中方法改善传统视觉近邻标注方法中的视觉相似而语义不相似的问题,有效提升准确率和准确预测的标签总数.     

A Multi-View Embedding Space for Modeling Internet Images,Tags, and Their Semantics

This paper investigates the problem of modeling Internet images and associated text or tags for tasks such as image-to-image search, tag-to-image search, and image-to-tag search (image annotation). We start with canonical correlation analysis (CCA), a popular and successful approach for mapping visual and textual features to the same latent space, and incorporate a third view capturing high-level image semantics, represented either by a single category or multiple non-mutually-exclusive concepts. We present two ways to train the three-view embedding: supervised, with the third view coming from ground-truth labels or search keywords; and unsupervised, with semantic themes automatically obtained by clustering the tags. To ensure high accuracy for retrieval tasks while keeping the learning process scalable, we combine multiple strong visual features and use explicit nonlinear kernel mappings to efficiently approximate kernel CCA. To perform retrieval, we use a specially designed similarity function in the embedded space, which substantially outperforms the Euclidean distance. The resulting system produces compelling qualitative results and outperforms a number of two-view baselines on retrieval tasks on three large-scale Internet image datasets.     

Tagging image by merging multiple features in a integrated manner

Image tagging is a task that automatically assigns the query image with semantic keywords called tags, which significantly facilitates image search and organization. Since tags and image visual content are represented in different feature space, how to merge the multiple features by their correlation to tag the query image is an important problem. However, most of existing approaches merge the features by using a relatively simple mechanism rather than fully exploiting the correlations between different features. In this paper, we propose a new approach to fusing different features and their correlation simultaneously for image tagging. Specifically, we employ a Feature Correlation Graph to capture the correlations between different features in an integrated manner, which take features as nodes and their correlations as edges. Then, a revised probabilistic model based on Markov Random Field is used to describe the graph for evaluating tag??s relevance to query image. Based on that, we design an image tagging algorithm for large scale web image dataset. We evaluate our approach using two large real-life corpuses collected from Flickr, and the experimental results indicate the superiority of our proposed approach over state-of-the-art techniques.     

Mining multi-tag association for image tagging

Automatic media tagging plays a critical role in modern tag-based media retrieval systems. Existing tagging schemes mostly perform tag assignment based on community contributed media resources, where the tags are provided by users interactively. However, such social resources usually contain dirty and incomplete tags, which severely limit the performance of these tagging methods. In this paper, we propose a novel automatic image tagging method aiming to automatically discover more complete tags associated with information importance for test images. Given an image dataset, all the near-duplicate clusters are discovered. For each near-duplicate cluster, all the tags occurring in the cluster form the cluster’s “document”. Given a test image, we firstly initialize the candidate tag set from its near-duplicate cluster’s document. The candidate tag set is then expanded by considering the implicit multi-tag associations mined from all the clusters’ documents, where each cluster’s document is regarded as a transaction. To further reduce noisy tags, a visual relevance score is also computed for each candidate tag to the test image based on a new tag model. Tags with very low scores can be removed from the final tag set. Extensive experiments conducted on a real-world web image dataset—NUS-WIDE, demonstrate the promising effectiveness of our approach.     

基于LDA和卷积神经网络的半监督图像标注方法

随着智能设备的不断出现,图像数量急速增加,但是很多图像因为没有被标注所以未被充分利用.为了能够使该问题得到较好解决,提出了基于LDA和卷积神经网络的半监督图像标注方法.首先把图像训练集中的所有文字信息放入LDA中,生成图像的文字标注词;然后使用卷积神经网络获得图像的高层视觉特征,同时用加入注意力机制和修改损失函数的方法...     

Support vector description of clusters for content-based image annotation

Continual progress in the fields of computer vision and machine learning has provided opportunities to develop automatic tools for tagging images; this facilitates searching and retrieving. However, due to the complexity of real-world image systems, effective and efficient image annotation is still a challenging problem. In this paper, we present an annotation technique based on the use of image content and word correlations. Clusters of images with manually tagged words are used as training instances. Images within each cluster are modeled using a kernel method, in which the image vectors are mapped to a higher-dimensional space and the vectors identified as support vectors are used to describe the cluster. To measure the extent of the association between an image and a model described by support vectors, the distance from the image to the model is computed. A closer distance indicates a stronger association. Moreover, word-to-word correlations are also considered in the annotation framework. To tag an image, the system predicts the annotation words by using the distances from the image to the models and the word-to-word correlations in a unified probabilistic framework. Simulated experiments were conducted on three benchmark image data sets. The results demonstrate the performance of the proposed technique, and compare it to the performance of other recently reported techniques.