Non-negative locality-constrained vocabulary tree for finger vein image retrieval

Finger vein image retrieval is a biometric identification technology that has recently attracted a lot of attention. It has the potential to reduce the search space and has attracted a considerable amount of research effort recently. It is a challenging problem owing to the large number of images in biometric databases and the lack of efficient retrieval schemes. We apply a hierarchical vocabulary tree modelbased image retrieval approach because of its good scalability and high efficiency.However, there is a large accumulative quantization error in the vocabulary tree (VT)model thatmay degrade the retrieval precision. To solve this problem, we improve the vector quantization coding in the VT model by introducing a non-negative locality-constrained constraint: the non-negative locality-constrained vocabulary tree-based image retrieval model. The proposed method can effectively improve coding performance and the discriminative power of local features. Extensive experiments on a large fused finger vein database demonstrate the superiority of our encoding method. Experimental results also show that our retrieval strategy achieves better performance than other state-of-theart methods, while maintaining low time complexity.     

基于粒子群优化的多特征融合的商标图像检索

提出一种基于粒子群优化的多特征融合的商标图像检索方法,该方法可自动优化多特征融合的权重,提高图像检索系统的自适应性,解决了多特征商标图像检索中的权重分配问题。在1 000幅图像构成的商标图像库进行检索实验,实验结果表明,与基于单一特征的检索方法和一些多特征融合的检索方法相比,提出方法的检索性能最优。     

综合距离和相关性的图像检索算法

在基于内容的图像检索中,大多数都是采用距离来测试两幅图像的相似性.提出了一种新的计算相关系数的方法并结合这种方法和距离来判断两幅图像的相似性,将其应用于CBIR(content-based image retrieval)系统.在对所提出的算法进行的实验中,用了10 000幅图像来测试了所提出的算法,实验结果表明:在同一个CBIR系统中,引入相关性能够提高图像的检索精度,解决了只用距离来判断两幅图像相似性的不足,对于基于低级特征的图像检索系统是一个很好的改进.     

Adaptive image segmentation for region-based object retrieval using generalized Hough transform

Finding an object inside a target image by querying multimedia data is desirable, but remains a challenge. The effectiveness of region-based representation for content-based image retrieval is extensively studied in the literature. One common weakness of region-based approaches is that perform detection using low level visual features within the region and the homogeneous image regions have little correspondence to the semantic objects. Thus, the retrieval results are often far from satisfactory. In addition, the performance is significantly affected by consistency in the segmented regions of the target object from the query and database images. Instead of solving these problems independently, this paper proposes region-based object retrieval using the generalized Hough transform (GHT) and adaptive image segmentation. The proposed approach has two phases. First, a learning phase identifies and stores stable parameters for segmenting each database image. In the retrieval phase, the adaptive image segmentation process is also performed to segment a query image into regions for retrieving visual objects inside database images through the GHT with a modified voting scheme to locate the target visual object under a certain affine transformation. The learned parameters make the segmentation results of query and database images more stable and consistent. Computer simulation results show that the proposed method gives good performance in terms of retrieval accuracy, robustness, and execution speed.     

Indexing chromatic and achromatic patterns for content-based colour image retrieval

In this paper, we present a method to represent achromatic and chromatic image signals independently for content-based image indexing and retrieval for image database applications. Starting from an opponent colour representation, human colour vision theories and modern digital signal processing technologies are applied to develop a compact and computationally efficient visual appearance model for coloured image patterns. We use the model to compute the statistics of achromatic and chromatic spatial patterns of colour images for indexing and content-based retrieval. Two types of colour images databases, one colour texture database and another photography colour image database are used to evaluate the performance of the developed method in content-based image indexing and retrieval. Experimental results are presented to show that the new method is superior or competitive to state-of-the-art content-based image indexing and retrieval techniques.     

基于区域熵的图像检索

提出了一种新的基于区域熵的图像检索算法RECS，不仅利用图像的子块熵来描述图像的特性，而且依据熵信息的均值和方差将图像分割为高熵子图和低熵子图两部分。综合图像区域的颜色形状特征和分两步的图像检索过程，有效提高检索准确性的同时也节省了检索时间。实验结果表明，RECS算法对前景单一和前景复杂图像的检索效果同样令人满意。     

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.     

Toward Fine-grained Image Retrieval with Adaptive Deep Learning for Cultural Heritage Image

Fine-grained image classification is a challenging research topic because of the high degree of similarity among categories and the high degree of dissimilarity for a specific category caused by different poses and scales. A cultural heritage image is one of the fine-grained images because each image has the same similarity in most cases. Using the classification technique, distinguishing cultural heritage architecture may be difficult. This study proposes a cultural heritage content retrieval method using adaptive deep learning for fine-grained image retrieval. The key contribution of this research was the creation of a retrieval model that could handle incremental streams of new categories while maintaining its past performance in old categories and not losing the old categorization of a cultural heritage image. The goal of the proposed method is to perform a retrieval task for classes. Incremental learning for new classes was conducted to reduce the re-training process. In this step, the original class is not necessary for re-training which we call an adaptive deep learning technique. Cultural heritage in the case of Thai archaeological site architecture was retrieved through machine learning and image processing. We analyze the experimental results of incremental learning for fine-grained images with images of Thai archaeological site architecture from world heritage provinces in Thailand, which have a similar architecture. Using a fine-grained image retrieval technique for this group of cultural heritage images in a database can solve the problem of a high degree of similarity among categories and a high degree of dissimilarity for a specific category. The proposed method for retrieving the correct image from a database can deliver an average accuracy of 85 percent. Adaptive deep learning for fine-grained image retrieval was used to retrieve cultural heritage content, and it outperformed state-of-the-art methods in fine-grained image retrieval.     

基于Hadoop的海量医学图像检索系统

为了提高海量医学图像检索效率,针对单节点医学图像检索系统的缺陷,提出一种基于Hadoop的海量医学图像检索系统。首先采用Brushlet变换和局部二值模式算法提取医学示例图像特征,并将图像特征库存储于Hadoop分布式文件系统(HDFS);然后采用Map将示例图像特征与特征库的特征进行匹配,采用Reduce接收各Map任务的计算结果,并按相似度大小进行排序;最后根据排序结果找到医学图像的最优检索结果。实验结果表明,相对于其他医学图像检索系统,Hadoop的医学图像检索系统减少了图像存储和检索时间,提高了图像检索速度。     

基于二值信息的颜色和形状特征的图像检索

由于单一特征不足以准确地描述图像,提出了一种结合颜色、形状特征的图像检索方法.提出了新的用二值信息来表示图像的主色、全局色和形状特征的方法,并由此特征构造两个过滤器快速地过滤图像库中明显不相同的图像,以提高检索速度;采用改进的颜色直方图和形状基本特征进行相似度计算,为进一步提高图像检索的质量引入相关反馈机制,提出了一种动态调整两幅图像相似度中颜色特征和形状特征的权值系数的方法.文中方法与其它方法进行了比较实验,结果表明,该方法优于其它方法.     

基于时域-频域哈希编码的电网图像检索方法

电网数据信息的准确检索在保障电网系统正常运行方面起着非常重要的作用。快速准确地从电网图像数据库中查找到与目标图像相似度高的图像可以有效地提高电网工作人员的工作效率,降低设备维护成本。针对传统检索方法检索精度低的问题,提出了一种基于时域-频域的端到端哈希编码方法。最后,在2个数据集上将该方法与最新的8种方法进行了比较,实验结果表明该方法是有效的。该方法创新性地结合了频域信息,以提高预测正确率,且结合了多任务学习和距圆损失来更加清晰地约束哈希编码任务的训练过程,使图像检索结果更加准确。     

基于PDTDFB变换的彩色纹理图像检索

为提高纹理图像的检索效率,提出一种基于统计模型的纹理特征提取方法.采用金字塔对偶树方向滤波器组实现图像变换,利用Gamma分布对方向子带系数进行建模,通过矩估计方法得到分布参数,并作为纹理检索的特征,使用改进的KL距离度量相似性.在VisTex彩色纹理图像数据库上的检索结果表明,与DT-CWT小波变换相比,该方法的平均检索率较高.     

Medical image retrieval based on unclean image bags

Traditional content-based image retrieval (CBIR) scheme with assumption of independent individual images in large-scale collections suffers from poor retrieval performance. In medical applications, images usually exist in the form of image bags and each bag includes multiple relevant images of the same perceptual meaning. In this paper, based on these natural image bags, we explore a new scheme to improve the performance of medical image retrieval. It is feasible and efficient to search the bag-based medical image collection by providing a query bag. However, there is a critical problem of noisy images which may present in image bags and severely affect the retrieval performance. A new three-stage solution is proposed to perform the retrieval and handle the noisy images. In stage 1, in order to alleviate the influence of noisy images, we associate each image in the image bags with a relevance degree. In stage 2, a novel similarity aggregation method is proposed to incorporate image relevance and feature importance into the similarity computation process. In stage 3, we obtain the final image relevance in an adaptive way which can consider both image bag similarity and individual image similarity. The experiments demonstrate that the proposed approach can improve the image retrieval performance significantly.     

Supporting image retrieval framework with rule base system

The present paper introduces an image retrieval framework based on a rule base system. The proposed framework makes use of color and texture features, respectively called color co-occurrence matrix (CCM) and difference between pixels of scan pattern (DBPSP). 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 relevance feedback phase followed by a novel clustering refinement model. The images and their corresponding classes pass to a rule base system 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.     

基于内容的图像检索中SVM和Boosting方法集成应用

提出一种适用于图像内容检索的AdaBoostSVM算法。算法思想是采用支持向量机(SVM)作为AdaBoost算法的分量分类器;基于相关反馈检索机制,通过增加重要样本来模拟AdaBoost算法的权重调整方法。在包含2000幅图像的数据库中进行了检索实验,结果表明AdaBoostSVM算法能有效提高系统的检索性能。     

改进颜色和小波变换纹理的图像检索方法研究

针对传统图像检索无法体现对检索示例图像中多个不同对象的检索要求程度的问题,提出一种改进颜色特征和小波变换纹理特征的图像检索方法。首先提取出图像的多个感兴趣区域,由感兴趣的不同程度分别赋予不同大小的权值;然后提取颜色特征和纹理特征,分别用对应位置相似度计算、感兴趣区域与检索数据库中图像整体的相似度计算和整体检索示例图像与检索图像数据库中图像相似度计算三种不同方法计算出两幅图像的相似度,取最大的相似度作为两幅图像的最终相似度;对检索示例图像与检索数据库中每个图像的相似度按大小进行排序,选择最相似的图像作为检索结果。实验结果表明,该方法提高了对图像检索的性能,体现了个性化检索,对图像检索具有很好的效果。     

基于深度卷积神经网络的图像检索算法研究

为解决卷积神经网络在提取图像特征时所造成的特征信息损失,提高图像检索的准确率,提出了一种基于改进卷积神经网络LeNet-L的图像检索算法。首先,改进LeNet-5卷积神经网络结构,增加网络结构深度。然后,对深度卷积神经网络模型LeNet-L进行预训练,得到训练好的网络模型,进而提取出图像高层语义特征。最后,通过距离函数比较待检图像与图像库的相似度,得出相似图像。在Corel数据集上,与原模型以及传统的SVM主动学习图像检索方法相比,该图像检索方法有较高的准确性。经实验结果表明,改进后的卷积神经网络具有更好的检索效果。     

Utilizing similarity relationships among existing data for high accuracy processing of content-based image retrieval

Retrieving similar images based on its visual content is an important yet difficult problem. We propose in this paper a new method to improve the accuracy of content-based image retrieval systems. Typically, given a query image, existing retrieval methods return a ranked list based on the similarity scores between the query and individual images in the database. Our method goes further by relying on an analysis of the underlying connections among individual images in the database to improve this list. Initially, we consider each image in the database as a query and use an existing baseline method to search for its likely similar images. Then, the database is modeled as a graph where images are nodes and connections among possibly similar images are edges. Next, we introduce an algorithm to split this graph into stronger subgraphs, based on our notion of graph’s strength, so that images in each subgraph are expected to be truly similar to each other. We create for each subgraph a structure called integrated image which contains the visual features of all images in the subgraph. At query time, we compute the similarity scores not only between the query and individual database images but also between the query and the integrated images. The final similarity score of a database image is computed based on both its individual score and the score of the integrated image that it belongs to. This leads effectively to a re-ranking of the retrieved images. We evaluate our method on a common image retrieval benchmark and demonstrate a significant improvement over the traditional bag-of-words retrieval model.     

基于Nyström 低阶近似的半监督流形排序图像检索

实际图像检索过程中,用户提供的相关反馈有限,但存在大量未标记图像数据. 本文在前期半监督流形图像检索工作的基础上,提出一种基于Nystrm低阶 近似的半监督流形排序图像检索方法.通过采用半监督的流形正则化框架, 将图像数据嵌入到低维流形结构中进行分类排序,以充分利用大量未标记数据, 并兼顾分类误差、数据分布的几何结构以及分类函数的复杂性.针对半监督学习速度缓慢的问题, 基于Nystrm低阶近似对学习过程进行加速.在较大规模的Corel图像数据集上进行了检索实验, 实验结果表明该方法能获得较好的效果.