基于可判别超平面树的生成模型图像标注方法

图像语义的自动标注是一个具有挑战性的研究课题,目前常见的机器学习方法,如统计生成模型(generative model)与判别模型(discriminative model)都被用于该问题的研究中.然而由于语义鸿沟的存在、图像训练数据的不平衡性以及图像标注的多标签特性等问题,使得上述方法的性能都有待进一步提高.提出一种基于可判别超平面树的生成模型图像标注方法.该方法根据待标注目标图像的高生成概率邻域,建立局部超平面分类树,进而利用同层类间可判别信息,按自顶向下的层次分类得到待标注图像的语义相关图像集合.由此得到的相关类信息与新的生成模型框架相结合对待标注图像与语义关键词的联合概率进行估计,实现对目标图像的标注.其特点在于生成模型与判别模型方法得到了有效结合,可判别超平面树对隐含语义聚类的判别分析是对待标注图像的生成"邻域"的逐步求精过程,有效地提高了生成模型标注准确度;而对于判别分析难以解决的多标签分类、训练数据不平衡等问题,此方法通过联合概率估计自然地实现目标图像的多标签分配.在常用的包含5 000幅图像的ECCV2002数据集进行了实验,结果表明,与目前已知的具有较好标注效果的基于生成模型的MBRM模型(采用图像分割方法)以及基于辨别分析的ASVM-MIL相比,此方法的F1因子分别提高了14%和13%.     

基于t混合模型的自动图像标注

图像自动标注的实质是通过对图像视觉特征的分析来提取高层语义关键词用于表示图像的含义,从而使得现有图像检索问题转化为技术已经相当成熟的文本检索问题,在一定程度上解决了基于内容图像检索中存在的语义鸿沟问题.采用t混合模型在已标注好的训练图像集上计算图像区域类与关键字的联合概率分布,在此基础上,对未曾观察过的测试图像集,利用生成的模型根据贝叶斯最小错误概率准则实现自动图像标注.实验结果表明,该方法能有效改善标注结果.     

基于相关视觉关键词的图像自动标注方法研究

图像自动标注是计算机视觉与模式识别等领域中的重要问题.针对现有模型未对文本关键词的视觉描述形式进行建模,导致标注结果中大量出现与图像视觉内容无关的标注词等问题,提出了基于相关视觉关键词的图像自动标注模型VKRAM.该模型将标注词分为非抽象标注词与抽象标注词.首先建立非抽象标注词的视觉关键词种子,并提出了一个新方法抽取非抽象标注词对应的视觉关键词集合;接着根据抽象关键词的特点,运用提出的基于减区域的算法抽取抽象关键词对应的视觉关键词种子与视觉关键词集合;然后提出一个自适应参数方法与快速求解算法用于确定不同视觉关键词的相似度阈值;最后将上述方法相结合并用于图像自动标注中.该模型能从一定程度上解决标注结果中出现的大量无关标注词问题.实验结果表明,该模型在大多数指标上相比以往模型均有所提高.     

场景语义树图像标注方法

自动图像标注是一项具有挑战性的工作,它对于图像分析理解和图像检索都有着重要的意义.在自动图像标注领域,通过对已标注图像集的学习,建立语义概念空间与视觉特征空间之间的关系模型,并用这个模型对未标注的图像集进行标注.由于低高级语义之间错综复杂的对应关系,使目前自动图像标注的精度仍然较低.而在场景约束条件下可以简化标注与视觉特征之间的映射关系,提高自动标注的可靠性.因此提出一种基于场景语义树的图像标注方法.首先对用于学习的标注图像进行自动的语义场景聚类,对每个场景语义类别生成视觉场景空间,然后对每个场景空间建立相应的语义树.对待标注图像,确定其语义类别后,通过相应的场景语义树,获得图像的最终标注.在Corel5K图像集上,获得了优于TM(translation model)、CMRM(cross media relevance model)、CRM(continous-space relevance model)、PLSA-GMM(概率潜在语义分析-高期混合模型)等模型的标注结果.     

一种新的图像语义自动标注与检索算法*

提出了一种新的利用图像语义词汇表进行图像自动标注与检索的方法.采用混合层次模型在已标注好的训练图像集上计算图像区域类与关键字的联合概率分布,并用生成的模型标注未曾观察过的测试图像集,或用来进行基于语义的图像检索.实验结果表明,该方法在标注、检索精度和效率方面均优于当前其他方法.     

基于高斯混合模型的自动图像标注方法

为了进一步完善自动图像标注方法,提出基于高斯混合模型的自动图像标注方法。该方法通过建立每个关键词唯一的高斯混合模型(GMM),准确地描述关键词的语义内容,进而提高自动图像标注的精确性。最后,通过采用COREL图像数据集与不同方法的比较,从平均查准率、平均查全率的实验结果验证了该方法的有效性。     

基于HMM的自动图像标注方法

自动图像标注技术已经成为弥补"语义鸿沟"的一种有效途径.提出基于隐马尔科夫模型HMM(Hidden Markov Model)的自动图像标注方法,不仅有效地挖掘关键词的语义视觉特征分布,从而建立图像-关键词的对应关系;而且通过融合关键词的共生关系,高效地获取关键词-关键词的语义关联.为此,建立图像-关键词与关键词-关键词的多视角相关模型,有助于解决自动图像标注任务.最后,在COREL图像数据集上的一系列实验结果,验证了提出方法的有效性.     

面向图像自动语句标注的注意力反馈模型

图像自动语句标注利用计算机自动生成描述图像内容的语句,在服务机器人等领域有广泛应用.许多学者已经提出了一些基于注意力机制的算法,但是注意力分散问题以及由注意力分散引起的生成语句错乱问题还未得到较好解决.在传统注意力机制的基础上引入注意力反馈机制,利用关注信息的图像特征指导文本生成,同时借助生成文本中的关注信息进一步修正图像中的关注区域,该过程不断强化图像和文本中的关键信息匹配、优化生成的语句.针对常用数据集Flickr8k, Flickr30k和MSCOCO的实验结果表明,该模型在一定程度上解决了注意力分散和语句顺序错乱问题,比其他基于注意力机制方法标注的关注区域更加准确,生成语句更加通顺.     

一种新的图像语义自动标注模型

根据图像低层特征和高级语义间的对应关系,自动进行图像语义标注是目前图像检索系统研究的热点。简要介绍了基于图像语义连接网络的图像检索框架,提出了一种基于该框架的图像自动标注模型。该模型通过积累用户反馈信息,学习并获得图像语义,从而进行自动的图像标注。图像语义及标注可以在与用户交互过程中得到实时更新。还提出了一种词义相关度分析的方法剔除冗余标注词,解决标注误传播的问题。通过在Corel图像集上的对比实验,验证了该方法的有效性。     

词间相关性的CMRM图像标注方法

自动图像标注因其对图像理解和网络图像检索的重要意义,近年来已成为新的热点研究课题.在图像标注的CMRM模型基础上,提出了一种基于词间相关性的CMRM标注方法.该方法提取了标注字之间的词间相关关系,并利用图学习算法,通过将词间相关性矩阵叠加到初始标注矩阵的方法对标注结果进行了改善.利用Corel5k标注图像库中的自然场景图像进行实验.实验结果表明,该方法很好地完成了对测试集图像的自动标注,在查全率与查准率上较CMRM模型有所提高.     

A two-level model for automatic image annotation

Image automatic annotation is a significant and challenging problem in pattern recognition and computer vision. Current image annotation models almost used all the training images to estimate joint generation probabilities between images and keywords, which would inevitably bring a lot of irrelevant images. To solve the above problem, we propose a hierarchical image annotation model which combines advantages of discriminative model and generative model. In first annotation layer, discriminative model is used to assign topic annotations to unlabeled images, and then relevant image set corresponding to each unlabeled image is obtained. In second annotation layer, we propose a keywords-oriented method to establish links between images and keywords, and then our iterative algorithm is used to expand relevant image sets. Candidate labels will be given higher weights by using our method based on visual keywords. Finally, generative model is used to assign detailed annotations to unlabeled images on expanded relevant image sets. Experiments conducted on Corel 5K datasets verify the effectiveness of our hierarchical image annotation model.     

Effective image annotation for searches using multilevel semantics

There is an increasing need for automatic image annotation tools to enable effective image searching in digital libraries. In this paper, we present a novel probabilistic model for image annotation based on content-based image retrieval techniques and statistical analysis. One key difficulty in applying statistical methods to the annotation of images is that the number of manually labeled images used to train the methods is normally insufficient. Numerous keywords cannot be correctly assigned to appropriate images due to lacking or missing information in the labeled image databases. To deal with this challenging problem, we also propose an enhanced model in which the annotated keywords of a new image are defined in terms of their similarity at different semantic levels, including the image level, keyword level, and concept level. To avoid missing some relevant keywords, the model labels the keywords with the same concepts as the new image. Our experimental results show that the proposed models are effective for annotating images that have different qualities of training data.     

Image annotation via graph learning

Image annotation has been an active research topic in recent years due to its potential impact on both image understanding and web image search. In this paper, we propose a graph learning framework for image annotation. First, the image-based graph learning is performed to obtain the candidate annotations for each image. In order to capture the complex distribution of image data, we propose a Nearest Spanning Chain (NSC) method to construct the image-based graph, whose edge-weights are derived from the chain-wise statistical information instead of the traditional pairwise similarities. Second, the word-based graph learning is developed to refine the relationships between images and words to get final annotations for each image. To enrich the representation of the word-based graph, we design two types of word correlations based on web search results besides the word co-occurrence in the training set. The effectiveness of the proposed solution is demonstrated from the experiments on the Corel dataset and a web image dataset.     

一种基于深度学习的半监督分层模型

依照图像识别出的对象标签,通过层次结构来分类图像集是图像自动化分类的重要研究问题之一。现有的方法实现了对象标签已知情况下的层次结构构建,仅存在少量方法考虑部分对象标签未知的影响。本文对经典方法进行了扩展和优化,实现了存在部分对象标签未知情况下的层次结构构建和更新。利用卷积神经网络(Convolutional neural network, CNN)对图像编码,提出半监督学习方法,根据传统算法构建类标签已知图像集的层次结构,通过周期性相似性比较,对层次结构中标签未知图像进行聚类,实现对半监督分层模型（Semi-supervised layer-wise model,SLM）的构建。本文采用了真实公开的数据集,实验结果表明,该方法能够有效地实现层次结构的构建和更新,并且能够在较小规模的数据集上取得好的预测分类效果。     

基于本体的图像自动标注

提出一种新的图像本体标注的框架,结合领域本体中概念的关系,通过层次概率标注来获得图像高层语义概念的标注,实现待标注图像语义的自动标注。我们将图像的语义可以定义为属性概念和高层抽象概念,采用二次标注方法实现对于图像语义的自动标注。实验证明,本文的方法可以使图像获得丰富的高层抽象语义概念标注,从而缩小"语义鸿沟",有效提高了检索的效率和精确度。     

Scalable search-based image annotation

With the popularity of digital cameras, more and more people have accumulated considerable digital images on their personal devices. As a result, there are increasing needs to effectively search these personal images. Automatic image annotation may serve the goal, for the annotated keywords could facilitate the search processes. Although many image annotation methods have been proposed in recent years, their effectiveness on arbitrary personal images is constrained by their limited scalability, i.e. limited lexicon of small-scale training set. To be scalable, we propose a search-based image annotation algorithm that is analogous to information retrieval. First, content-based image retrieval technology is used to retrieve a set of visually similar images from a large-scale Web image set. Second, a text-based keyword search technique is used to obtain a ranked list of candidate annotations for each retrieved image. Third, a fusion algorithm is used to combine the ranked lists into a final candidate annotation list. Finally, the candidate annotations are re-ranked using Random Walk with Restarts and only the top ones are reserved as the final annotations. The application of both efficient search techniques and Web-scale image set guarantees the scalability of the proposed algorithm. Moreover, we provide an annotation rejection scheme to point out the images that our annotation system cannot handle well. Experimental results on U. Washington dataset show not only the effectiveness and efficiency of the proposed algorithm but also the advantage of image retrieval using annotation results over that using visual features.     

Incorporating multiple SVMs for automatic image annotation

In this paper, a novel automatic image annotation system is proposed, which integrates two sets of support vector machines (SVMs), namely the multiple instance learning (MIL)-based and global-feature-based SVMs, for annotation. The MIL-based bag features are obtained by applying MIL on the image blocks, where the enhanced diversity density (DD) algorithm and a faster searching algorithm are applied to improve the efficiency and accuracy. They are further input to a set of SVMs for finding the optimum hyperplanes to annotate training images. Similarly, global color and texture features, including color histogram and modified edge histogram, are fed into another set of SVMs for categorizing training images. Consequently, two sets of image features are constructed for each test image and are, respectively, sent to the two sets of SVMs, whose outputs are incorporated by an automatic weight estimation method to obtain the final annotation results. Our proposed annotation approach demonstrates a promising performance for an image database of 12 000 general-purpose images from COREL, as compared with some current peer systems in the literature.     

Effective and Efficient Multi-Facet Web Image Annotation

The vast amount of images available on the Web request for an effective and efficient search service to help users find relevant images.The prevalent way is to provide a keyword interface for users to submit queries.However,the amount of images without any tags or annotations are beyond the reach of manual efforts.To overcome this,automatic image annotation techniques emerge,which are generally a process of selecting a suitable set of tags for a given image without user intervention.However,there are three main challenges with respect to Web-scale image annotation:scalability,noiseresistance and diversity.Scalability has a twofold meaning:first an automatic image annotation system should be scalable with respect to billions of images on the Web;second it should be able to automatically identify several relevant tags among a huge tag set for a given image within seconds or even faster.Noise-resistance means that the system should be robust enough against typos and ambiguous terms used in tags.Diversity represents that image content may include both scenes and objects,which are further described by multiple different image features constituting different facets in annotation.In this paper,we propose a unified framework to tackle the above three challenges for automatic Web image annotation.It mainly involves two components:tag candidate retrieval and multi-facet annotation.In the former content-based indexing and concept-based codebook are leveraged to solve scalability and noise-resistance issues.In the latter the joint feature map has been designed to describe different facets of tags in annotations and the relations between these facets.Tag graph is adopted to represent tags in the entire annotation and the structured learning technique is employed to construct a learning model on top of the tag graph based on the generated joint feature map.Millions of images from Flickr are used in our evaluation.Experimental results show that we have achieved 33% performance improvements compared with those single facet approaches in terms of three metrics:precision,recall and F1 score.