A unified log-based relevance feedback scheme for image retrieval

Relevance feedback has emerged as a powerful tool to boost the retrieval performance in content-based image retrieval (CBIR). In the past, most research efforts in this field have focused on designing effective algorithms for traditional relevance feedback. Given that a CBIR system can collect and store users' relevance feedback information in a history log, an image retrieval system should be able to take advantage of the log data of users' feedback to enhance its retrieval performance. In this paper, we propose a unified framework for log-based relevance feedback that integrates the log of feedback data into the traditional relevance feedback schemes to learn effectively the correlation between low-level image features and high-level concepts. Given the error-prone nature of log data, we present a novel learning technique, named soft label support vector machine, to tackle the noisy data problem. Extensive experiments are designed and conducted to evaluate the proposed algorithms based on the COREL image data set. The promising experimental results validate the effectiveness of our log-based relevance feedback scheme empirically.     

Relevance Feedback and Learning in Content-Based Image Search

A major bottleneck in content-based image retrieval (CBIR) systems or search engines is the large gap between low-level image features used to index images and high-level semantic contents of images. One solution to this bottleneck is to apply relevance feedback to refine the query or similarity measures in image search process. In this paper, we first address the key issues involved in relevance feedback of CBIR systems and present a brief overview of a set of commonly used relevance feedback algorithms. Almost all of the previously proposed methods fall well into such framework. We present a framework of relevance feedback and semantic learning in CBIR. In this framework, low-level features and keyword annotations are integrated in image retrieval and in feedback processes to improve the retrieval performance. We have also extended framework to a content-based web image search engine in which hosting web pages are used to collect relevant annotations for images and users' feedback logs are used to refine annotations. A prototype system has developed to evaluate our proposed schemes, and our experimental results indicated that our approach outperforms traditional CBIR system and relevance feedback approaches.     

A new relevance feedback technique for iconic image retrieval based on spatial relationships

Due to the popularity of Internet and the growing demand of image access, the volume of image databases is exploding. Hence, we need a more efficient and effective image searching technology. Relevance feedback technique has been popularly used with content-based image retrieval (CBIR) to improve the precision performance, however, it has never been used with the retrieval systems based on spatial relationships. Hence, we propose a new relevance feedback framework to deal with spatial relationships represented by a specific data structure, called the 2D B_e-string. The notions of relevance estimation and query reformulation are embodied in our method to exploit the relevance knowledge. The irrelevance information is collected in an irrelevant set to rule out undesired pictures and to expedite the convergence speed of relevance feedback. Our system not only handles picture-based relevance feedback, but also deals with region-based feedback mechanism, such that the efficacy and effectiveness of our retrieval system are both satisfactory.     

Collaborative image retrieval via regularized metric learning

In content-based image retrieval (CBIR), relevant images are identified based on their similarities to query images. Most CBIR algorithms are hindered by the semantic gap between the low-level image features used for computing image similarity and the high-level semantic concepts conveyed in images. One way to reduce the semantic gap is to utilize the log data of users' feedback that has been collected by CBIR systems in history, which is also called “collaborative image retrieval.” In this paper, we present a novel metric learning approach, named “regularized metric learning,” for collaborative image retrieval, which learns a distance metric by exploring the correlation between low-level image features and the log data of users' relevance judgments. Compared to the previous research, a regularization mechanism is used in our algorithm to effectively prevent overfitting. Meanwhile, we formulate the proposed learning algorithm into a semidefinite programming problem, which can be solved very efficiently by existing software packages and is scalable to the size of log data. An extensive set of experiments has been conducted to show that the new algorithm can substantially improve the retrieval accuracy of a baseline CBIR system using Euclidean distance metric, even with a modest amount of log data. The experiment also indicates that the new algorithm is more effective and more efficient than two alternative algorithms, which exploit log data for image retrieval.     

Rich Get Richer——图像检索中的一种自适应的相关反馈方法

早期的基于内容的图像检索系统以图像处理技术为核心,研究重点集中在视觉特征的选择和提取方面,而没有充分利用人们在视觉方面的主观性和人类所广泛使用的高层次概念和低层次视觉特征之间的相关性。为解决上述问题,近年来相关反馈在基于内容的图像检索中受到重视。提出了一种新的相关反馈方法,使得高层次语义特征能够逐步嵌入到基于 低层次特征的图像检索中,该方法不仅能够记忆以前的交互信息,而且能够记忆相应的交互信息给系统带来的影响,实验结果表明该方法准确率高、响应速度快。     

历史信息学习的相关反馈图像检索

相关反馈技术被有效的应用于基于内容的图像检索.传统的相关反馈未能充分利用检索的历史信息.为了进一步提高检索的效率与准确性,提出一种基于历史检索信息学习的相关反馈检索方法.该方法将每次检索的结果作为历史检索信息保存.进行新的检索时,判断当前查询图像与历史检索信息的语义相关性,预测检索结果,以期减少相关反馈次数.对包含80 00幅图像的图像库实验表明,与传统相关反馈技术相比,该方法明显的改善了检索性能.     

Vector space model adaptation and pseudo relevance feedback for content-based image retrieval

Image retrieval is an important problem for researchers in computer vision and content-based image retrieval (CBIR) fields. Over the last decades, many image retrieval systems were based on image representation as a set of extracted low-level features such as color, texture and shape. Then, systems calculate similarity metrics between features in order to find similar images to a query image. The disadvantage of this approach is that images visually and semantically different may be similar in the low level feature space. So, it is necessary to develop tools to optimize retrieval of information. Integration of vector space models is one solution to improve the performance of image retrieval. In this paper, we present an efficient and effective retrieval framework which includes a vectorization technique combined with a pseudo relevance model. The idea is to transform any similarity matching model (between images) to a vector space model providing a score. A study on several methodologies to obtain the vectorization is presented. Some experiments have been undertaken on Wang, Oxford5k and Inria Holidays datasets to show the performance of our proposed framework.     

基于内容的图象检索技术

随着数字图象的日益增多,基于内容的图象检索已成为图象使用者和管理者迫切需要解决的问题,近年来,各国研究者纷纷加入该领域的研究.为了使人们对该领域现状有个概略了解,以推动该领域研究进一步开展,首先概括介绍了基于内容图象检索的产生、发展及其关键技术;然后介绍了特征提取(包括低层特征和语义特征)及其相似性计算、相关反馈等的原理及算法;最后指出了基于内容的图象检索技术与计算机视觉技术的区别所在,并对目前存在的问题和应着重的研究内容以及发展方向进行了分析.     

An image retrieval system with automatic query modification

Most interactive "query-by-example" based image retrieval systems utilize relevance feedback from the user for bridging the gap between the user's implied concept and the low-level image representation in the database. However, traditional relevance feedback usage in the context of content-based image retrieval (CBIR) may not be very efficient due to a significant overhead in database search and image download time in client-server environments. In this paper, we propose a CBIR system that efficiently addresses the inherent subjectivity in user perception during a retrieval session by employing a novel idea of intra-query modification and learning. The proposed system generates an object-level view of the query image using a new color segmentation technique. Color, shape and spatial features of individual segments are used for image representation and retrieval. The proposed system automatically generates a set of modifications by manipulating the features of the query segment(s). An initial estimate of user perception is learned from the user feedback provided on the set of modified images. This largely improves the precision in the first database search itself and alleviates the overheads of database search and image download. Precision-to-recall ratio is improved in further iterations through a new relevance feedback technique that utilizes both positive as well as negative examples. Extensive experiments have been conducted to demonstrate the feasibility and advantages of the proposed system.     

综合颜色、纹理、形状和相关反馈的图像检索*

提出了一种综合颜色、纹理和形状三种特征并进行多层检索的方法;同时,将相关反馈技术融合到算法中,通过调整特征间和特征内的权重来提高检索准确率.实现了一个图像检索原型系统,将不同实验结果进行了比较和分析.实验结果表明,提出的方法具有良好的检索效果.     

An improved distance-based relevance feedback strategy for image retrieval

Most CBIR (content based image retrieval) systems use relevance feedback as a mechanism to improve retrieval results. NN (nearest neighbor) approaches provide an efficient method to compute relevance scores, by using estimated densities of relevant and non-relevant samples in a particular feature space. In this paper, particularities of the CBIR problem are exploited to propose an improved relevance feedback algorithm based on the NN approach. The resulting method has been tested in a number of different situations and compared to the standard NN approach and other existing relevance feedback mechanisms. Experimental results evidence significant improvements in most cases.     

A novel relevance feedback method for CBIR

In this paper, we address the challenge about insufficiency of training set and limited feedback information in each relevance feedback (RF) round during the process of content based image retrieval (CBIR). We propose a novel active learning scheme to utilize the labeled and unlabeled images to build the initial Support Vector Machine (SVM) classifier for image retrieving. In our framework, two main components, a pseudo-label strategy and an improved active learning selection method, are included. Moreover, a feature subspace partition algorithm is proposed to model the retrieval target from users by the analysis from relevance labeled images. Experimental results demonstrate the superiority of the proposed method on a range of databases with respect to the retrieval accuracy.     

一种基于内容的图像检索界面

基于内容和对象的图像压缩和检索是下一代的图像处理技术,具有较广阔的应用前景。目前该领域的研究主要从设计方便、快捷的用户查询界面和发展图像数据库检索技术两方面展开。为此,该文提出了一种基于内容的图像检索用户界面的设计方法来满足用户复杂的检索要求。在图像的检索过程中,通过用户组合图标的方法来描述检索要求,同时将图像的颜色和空间信息相结合进行图像查询,并借助用户的反馈信息实现系统的自学习功能,最终逐步提高系统图像检索的速度和准确性。     

一种基于记忆的图像检索相关反馈策略

相关反馈在基于内容的图像检索中成为提升检索效率的一项重要技术。然而,在图像检索中,高层语义与底层特征之间存在着巨大的"语义鸿沟",传统的相关反馈技术需要多次反馈才能获得满意结果,这使得用户的检索任务既耗时且繁琐。因此,本文通过对图像检索反馈日志信息的存储及使用过程进行分析,提出一种新的基于记忆的相关反馈策略。通过原型系统实验,与传统反馈策略相比,本文提出的策略对检索效率有明显改善。     

A multiple instance learning based framework for semantic image segmentation

Most image segmentation algorithms extract regions satisfying visual uniformity criteria. Unfortunately, because of the semantic gap between low-level features and high-level semantics, such regions usually do not correspond to meaningful parts. This has motivated researchers to develop methods that, by introducing high-level knowledge into the segmentation process, can break through the performance ceiling imposed by the semantic gap. The main disadvantage of those methods is their lack of flexibility due to the assumption that such knowledge is provided in advance. In content-based image retrieval (CBIR), relevance feedback (RF) learning has been successfully applied as a technique aimed at reducing the semantic gap. Inspired by this, we present a RF-based CBIR framework that uses multiple instance learning to perform a semantically-guided context adaptation of segmentation parameters. A partial instantiation of this framework that uses mean shift-based segmentation is presented. Experiments show the effectiveness and flexibility of the proposed framework on real images.     

Embedding neural networks for semantic association in content based image retrieval

Content based image retrieval (CBIR) systems provide potential solution of retrieving semantically similar images from large image repositories against any query image. The research community are competing for more effective ways of content based image retrieval, so they can be used in serving time critical applications in scientific and industrial domains. In this paper a Neural Network based architecture for content based image retrieval is presented. To enhance the capabilities of proposed work, an efficient feature extraction method is presented which is based on the concept of in-depth texture analysis. For this wavelet packets and Eigen values of Gabor filters are used for image representation purposes. To ensure semantically correct image retrieval, a partial supervised learning scheme is introduced which is based on K-nearest neighbors of a query image, and ensures the retrieval of images in a robust way. To elaborate the effectiveness of the presented work, the proposed method is compared with several existing CBIR systems, and it is proved that the proposed method has performed better then all of the comparative systems.

     

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.