Automatic image annotation and retrieval using group sparsity

Automatically assigning relevant text keywords to images is an important problem. Many algorithms have been proposed in the past decade and achieved good performance. Efforts have focused upon model representations of keywords, whereas properties of features have not been well investigated. In most cases, a group of features is preselected, yet important feature properties are not well used to select features. In this paper, we introduce a regularization-based feature selection algorithm to leverage both the sparsity and clustering properties of features, and incorporate it into the image annotation task. Using this group-sparsity-based method, the whole group of features [e.g., red green blue (RGB) or hue, saturation, and value (HSV)] is either selected or removed. Thus, we do not need to extract this group of features when new data comes. A novel approach is also proposed to iteratively obtain similar and dissimilar pairs from both the keyword similarity and the relevance feedback. Thus, keyword similarity is modeled in the annotation framework. We also show that our framework can be employed in image retrieval tasks by selecting different image pairs. Extensive experiments are designed to compare the performance between features, feature combinations, and regularization-based feature selection methods applied on the image annotation task, which gives insight into the properties of features in the image annotation task. The experimental results demonstrate that the group-sparsity-based method is more accurate and stable than others.     

基于特征加权的自动图像分类方法

低层特征的选择与提取是自动图像分类的基础,一方面,所选择的图像特征应能代表各种不同的图像属性,利于不同类别图像之间的区分;另一方面,为了提高后续模型的计算效率,需要减少噪声特征、冗余特征.提出了一种基于特征加权的自动图像分类方法.该方法根据图像低层特征分布的离散程度来衡量特征相对于类别的重要性,增加相关度高的特征的权重,降低相关度低的特征权重,从而避免后续模型被弱相关或不相关的特征所支配.所提的特征加权算法主要考察的是特征相对某个具体类别的重要程度,可以为每个类别选择出适合自身的特征权重.然后,将加权特征嵌入到支持向量机算法中用于自动图像分类,在Corel图像数据集上的实验结果表明,基于特征加权的自动图像分类算法可以有效地提高图像分类的准确性.     

An object-based image retrieval system for digital libraries

A novel approach to clustering for image segmentation and a new object-based image retrieval method are proposed. The clustering is achieved using the Fisher discriminant as an objective function. The objective function is improved by adding a spatial constraint that encourages neighboring pixels to take on the same class label. A six-dimensional feature vector is used for clustering by way of the combination of color and busyness features for each pixel. After clustering, the dominant segments in each class are chosen based on area and used to extract features for image retrieval. The color content is represented using a histogram, and Haar wavelets are used to represent the texture feature of each segment. The image retrieval is segment-based; the user can select a query segment to perform the retrieval and assign weights to the image features. The distance between two images is calculated using the distance between features of the constituent segments. Each image is ranked based on this distance with respect to the query image segment. The algorithm is applied to a pilot database of natural images and is shown to improve upon the conventional classification and retrieval methods. The proposed segmentation leads to a higher number of relevant images retrieved, 83.5% on average compared to 72.8 and 68.7% for the k-means clustering and the global retrieval methods, respectively.     

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.     

Unsupervised image retrieval framework based on rule base system

This paper introduces unsupervised image retrieval framework based on a rule base system. The proposed framework makes use of geometric moments (GMs) for features extraction. The main advantage with the GMs is that image coordinate transformations can be easily expressed and analyzed in terms of the corresponding transformations in the moment space. These features are used to perform the image mining for acquiring clustering knowledge from a large empirical images database. Irrelevance between images of the same cluster is precisely considered in the proposed framework through a relevant feedback phase followed by a novel clustering refinement model. The images and their corresponding classes pass to a rule base algorithm for extracting a set of accurate rules. These rules are pruning and may reduce the dimensionality of the extracted features. The advantage of the proposed framework is reflected in the retrieval process, which is limited to the images in the class of rule matched with the query image features. Experiments show that the proposed model achieves a very good performance in terms of the average precision, recall and retrieval time compared with other models.     

综合颜色和形状特征聚类的图像检索

为了提高图像检索的速度和准确率,通过分析各种聚类算法在图像检索中的缺点,提出了一种新的划分聚类的图像检索方法。首先对HSV模型非均匀量化,利用改进的颜色聚合向量方法提取图像的颜色特征;然后基于改进的Hu不变矩提取图像的全局形状特征;最后,综合颜色和形状特征对图像基于贡献度聚类并建立特征索引库。利用上述方法在Corel图像库中进行图像检索。实验结果表明,与改进的K-means算法的图像检索算法相比,提出算法的查准率和查全率均有较大提高。     

基于遗传特征选择和支持向量机的图像标注

为了提高图像标注系统的精度和效率,提出了基于遗传特征选择和支持向量机的图像标注方法。该方法从多媒体描述接口（MPEG-7）标准中抽取图像的视觉特征,采用双编码遗传算法从MPEG-7标准中选择最优的加权特征子集,并训练支持向量机分类器用于图像标注,支持向量机分类器采用多数投票机制。对2 000幅Corel图像的标注结果表明：该方法可以获得最优的加权特征子集,提高了图像标注系统的精度和效率。     

一种新的基于语义聚类和图算法的自动图像标注方法

针对图像检索中的语义鸿沟问题,提出了一种新颖的自动图像标注方法。该方法首先采用了一种基于软约束的半监督图像聚类算法(SHMRF-Kmeans)对已标注图像的区域进行语义聚类,这种聚类方法可以同时考虑图像的视觉信息和语义信息。并利用图算法——Manifold排序学习算法充分发掘语义概念与区域聚类中心的关系,得到两者的联合概率关系表。然后利用此概率关系表标注未知标注的图像。该方法与以前的方法相比可以更加充分地结合图像的视觉特征和高层语义。通过在通用图像集上的实验结果表明,本文提出的自动图像标注方法是有效的。     

基于判别模型与生成模型的层叠图像自动标注

图像自动标注是模式识别与计算机视觉等领域中重要而又具有挑战性的问题.针对现有模型存在数据利用率低与易受正负样本不平衡影响等问题,提出了基于判别模型与生成模型的新型层叠图像自动标注模型.该模型第一层利用判别模型对未标注图像进行主题标注,获得相应的相关图像集;第二层利用提出的面向关键词的方法建立图像与关键词之间的联系,并使用提出的迭代算法分别对语义关键词与相关图像进行扩展;最后利用生成模型与扩展的相关图像集对未标注图像进行详细标注.该模型综合了判别模型与生成模型的优点,通过利用较少的相关训练图像来获得更好的标注结果.在Corel 5K图像库上进行的实验验证了该模型的有效性.     

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.     

Supervised learning of semantic classes for image annotation and retrieval

A probabilistic formulation for semantic image annotation and retrieval is proposed. Annotation and retrieval are posed as classification problems where each class is defined as the group of database images labeled with a common semantic label. It is shown that, by establishing this one-to-one correspondence between semantic labels and semantic classes, a minimum probability of error annotation and retrieval are feasible with algorithms that are 1) conceptually simple, 2) computationally efficient, and 3) do not require prior semantic segmentation of training images. In particular, images are represented as bags of localized feature vectors, a mixture density estimated for each image, and the mixtures associated with all images annotated with a common semantic label pooled into a density estimate for the corresponding semantic class. This pooling is justified by a multiple instance learning argument and performed efficiently with a hierarchical extension of expectation-maximization. The benefits of the supervised formulation over the more complex, and currently popular, joint modeling of semantic label and visual feature distributions are illustrated through theoretical arguments and extensive experiments. The supervised formulation is shown to achieve higher accuracy than various previously published methods at a fraction of their computational cost. Finally, the proposed method is shown to be fairly robust to parameter tuning     

Image Annotation by Propagating Labels from Semantic Neighbourhoods

Automatic image annotation aims at predicting a set of semantic labels for an image. Because of large annotation vocabulary, there exist large variations in the number of images corresponding to different labels (“class-imbalance”). Additionally, due to the limitations of human annotation, several images are not annotated with all the relevant labels (“incomplete-labelling”). These two issues affect the performance of most of the existing image annotation models. In this work, we propose 2-pass k-nearest neighbour (2PKNN) algorithm. It is a two-step variant of the classical k-nearest neighbour algorithm, that tries to address these issues in the image annotation task. The first step of 2PKNN uses “image-to-label” similarities, while the second step uses “image-to-image” similarities, thus combining the benefits of both. We also propose a metric learning framework over 2PKNN. This is done in a large margin set-up by generalizing a well-known (single-label) classification metric learning algorithm for multi-label data. In addition to the features provided by Guillaumin et al. (2009) that are used by almost all the recent image annotation methods, we benchmark using new features that include features extracted from a generic convolutional neural network model and those computed using modern encoding techniques. We also learn linear and kernelized cross-modal embeddings over different feature combinations to reduce semantic gap between visual features and textual labels. Extensive evaluations on four image annotation datasets (Corel-5K, ESP-Game, IAPR-TC12 and MIRFlickr-25K) demonstrate that our method achieves promising results, and establishes a new state-of-the-art on the prevailing image annotation datasets.     

集成视觉特征和语义信息的相关反馈方法

为了有效地利用图像检索系统的语义分类信息和视觉特征,提出一种基于Bayes的集成视觉特征和语义信息的相关反馈检索方法.首先,将图像库的数据经语义监督的视觉特征聚类算法划分为小的聚类,每个聚类内数据的视觉特征相似并且语义类别相同;然后以聚类为单位标注正负反馈的实例,这显著区别于以单个图像为单位的相关反馈过程;最后分别以基于视觉特征的Bayes分类器和基于语义的Bayes分类器修正相似距离.在图像库上的实验表明,只用较少的反馈次数就可以达到较高的检索准确率.     

一种基于颜色统计聚类的医学图像检索技术

基于颜色检索的基本思想是将图像间的距离归结为其颜色直方图间的相似性度量,从而图像检索也就转化为颜色直方图的匹配。目前基于颜色检索的算法主要集中在不同颜色空间进行全局颜色聚类或融合其他可视特征(纹理,颜色空间信息等)联合检索两个方向上。该文在具体的结肠镜图像检索系统研究中,根据医学图像的特点,提出一种在HSV空间的颜色统计聚类的检索方法,取得了良好的检索效果。     

基于多尺度HOG的草图检索

草图检索是图像处理领域中的重要研究内容。提出了一种将高斯金字塔和局部HOG特征融合的特征提取改进方法,并将其用于草图检索。采用高斯金字塔将图像分解到多尺度空间,在所有尺度上进行兴趣点提取,获得基于兴趣点的多尺度HOG特征。利用图像的多尺度HOG特征集生成视觉词典,最终形成与视觉词典相关的特征描述向量,通过相似度匹配实现草图检索。将该算法与单一尺度下的HOG算法及其他几种算法比较,实验结果表明了其可行性和有效性。     

基于增强稀疏性特征选择的网络图像标注

面对网络图像的爆炸性增长,网络图像标注成为近年来一个热点研究内容,稀疏特征选择在提升网络图像标注效率和性能方面发挥着重要的作用.提出了一种增强稀疏性特征选择算法,即,基于l_2,1/2矩阵范数和共享子空间的半监督稀疏特征选择算法(semi-supervised sparse feature selection based on l_2,1/2-matix norm with shared subspace learning,简称SFSLS)进行网络图像标注.在SFSLS算法中,应用l_2,1/2矩阵范数来选取最稀疏和最具判别性的特征,通过共享子空间学习,考虑不同特征之间的关联信息.另外,基于图拉普拉斯的半监督学习,使SFSLS算法同时利用了有标签数据和无标签数据.设计了一种有效的迭代算法来最优化目标函数.SFSLS算法与其他稀疏特征选择算法在两个大规模网络图像数据库上进行了比较,结果表明,SFSLS算法更适合于大规模网络图像的标注.