共查询到10条相似文献,搜索用时 140 毫秒
1.
This paper presents a generalized Bayesian framework for relevance feedback in content-based image retrieval. The proposed feedback technique is based on the Bayesian learning method and incorporates a time-varying user model into the formulation. We define the user model with two terms: a target query and a user conception. The target query is aimed to learn the common features from relevant images so as to specify the user's ideal query. The user conception is aimed to learn a parameter set to determine the time-varying matching criterion. Therefore, at each feedback step, the learning process updates not only the target distribution, but also the target query and the matching criterion. In addition, another objective of this paper is to conduct the relevance feedback on images represented in region level. We formulate the matching criterion using a weighting scheme and proposed a region clustering technique to determine the region correspondence between relevant images. With the proposed region clustering technique, we derive a representation in region level to characterize the target query. Experiments demonstrate that the proposed method combined with time-varying user model indeed achieves satisfactory results and our proposed region-based techniques further improve the retrieval accuracy. 相似文献
2.
为弥合图像低层视觉特征和高层语义之间的语义鸿沟,改善图像检索的效果,机器学习算法经常被引入到图像检索问题中.通常情况下,机器学习算法是与相关反馈机制相结合,通过用户的交互操作,标定出若干正反例图像,很自然地就可以将图像检索问题转化为模式识别中的分类问题.目前融合区域显著性分析的区域图像检索算法尚没有与机器学习算法相融合.本文结合图像区域显著性分析,并针对用户参与反馈的情况,分别提出了两种图像检索解决方案.其一,在没有用户反馈以及用户只反馈正例图像的情形下,将图像检索问题转化为直推式学习问题(Transductive Learning),改进已有的基于图的半监督学习算法,提出了融合区域显著性分析的层次化图表示(Hierarchical Graph Representation)方式,用以实现标记传播;其二,在用户同时反馈正反例图像的情形下,利用用户反馈得到的正反例图像构建相似性邻接矩阵,通过流形排序算法(Manifold-Ranking)学习出用户感兴趣的查询目标概念并用相应的特征向量集合表示,并据此查询图像库返回用户语义相关的图像集合.实验结果验证了这两种检索策略的有效性. 相似文献
3.
Wei Jiang Guihua Er Qionghai Dai Jinwei Gu 《IEEE transactions on image processing》2006,15(3):702-712
Content-based image retrieval (CBIR) has been more and more important in the last decade, and the gap between high-level semantic concepts and low-level visual features hinders further performance improvement. The problem of online feature selection is critical to really bridge this gap. In this paper, we investigate online feature selection in the relevance feedback learning process to improve the retrieval performance of the region-based image retrieval system. Our contributions are mainly in three areas. 1) A novel feature selection criterion is proposed, which is based on the psychological similarity between the positive and negative training sets. 2) An effective online feature selection algorithm is implemented in a boosting manner to select the most representative features for the current query concept and combine classifiers constructed over the selected features to retrieve images. 3) To apply the proposed feature selection method in region-based image retrieval systems, we propose a novel region-based representation to describe images in a uniform feature space with real-valued fuzzy features. Our system is suitable for online relevance feedback learning in CBIR by meeting the three requirements: learning with small size training set, the intrinsic asymmetry property of training samples, and the fast response requirement. Extensive experiments, including comparisons with many state-of-the-arts, show the effectiveness of our algorithm in improving the retrieval performance and saving the processing time. 相似文献
4.
In region-based approaches to content-based image retrieval each image is segmented into a set of regions and similarity between
images is assessed by computing similarity between pairs of regions. A key factor in similarity measures that consider all
pairs of regions to obtain an overall image-to-image similarity is the weighting of regions. The weight that is assigned to
each region for determining similarity is usually based on heuristics that are often inconsistent with human perception of
similarity. In this paper, we propose an approach that uses relevance feedback in conjunction with multiple instance learning
to obtain more informed estimates of region weights. A comparative study is then carried out with alternative region re-weighting
methods. 相似文献
5.
贝叶斯框架下基于区域的相关反馈算法 总被引:2,自引:0,他引:2
融合基于区域的图像表达方式和相关反馈技术能够有效地提高图像检索的性能.由于现有的方法没有充分考虑相同语义类内区域特征的分布情况,进而无法对该类的语义信息进行有效的描述,为此该文提出了贝叶斯框架下基于区域的相关反馈模型.在每轮相关反馈中,通过在线学习区域的贝叶斯分类器,同时根据最近邻最小错误估计原则确定分类器的可信度,可以可靠地建立图像相似性度量的概率模型.此外,在应用非参数密度估计技术来构造语义类的特征分布时,针对区域分割的不精确性,该文还考虑了区域特征空间的总体分布因素,进而对区域的后验分布进行更可靠地估计.最后的实验说明了该文方法的有效性. 相似文献
6.
为了解决传统的CBIR系统中存在的"语义鸿沟"问题,提出一种基于潜在语义索引技术(LSI)和相关反馈技术的图像检索方法.在进行图像检索时,先在HSV空间下提取颜色直方图作为底层视觉特征进行图像检索,然后引入潜在语义索引技术试图将底层特征赋予更高层次的语义含义;并且结合相关反馈技术,通过与用户交互进一步提高检索精度.实验... 相似文献
7.
Feng Jing Mingling Li Hong-Jiang Zhang Bo Zhang 《IEEE transactions on image processing》2005,14(7):979-989
In this paper, a unified image retrieval framework based on both keyword annotations and visual features is proposed. In this framework, a set of statistical models are built based on visual features of a small set of manually labeled images to represent semantic concepts and used to propagate keywords to other unlabeled images. These models are updated periodically when more images implicitly labeled by users become available through relevance feedback. In this sense, the keyword models serve the function of accumulation and memorization of knowledge learned from user-provided relevance feedback. Furthermore, two sets of effective and efficient similarity measures and relevance feedback schemes are proposed for query by keyword scenario and query by image example scenario, respectively. Keyword models are combined with visual features in these schemes. In particular, a new, entropy-based active learning strategy is introduced to improve the efficiency of relevance feedback for query by keyword. Furthermore, a new algorithm is proposed to estimate the keyword features of the search concept for query by image example. It is shown to be more appropriate than two existing relevance feedback algorithms. Experimental results demonstrate the effectiveness of the proposed framework. 相似文献
8.
This paper considers the semantic gap in content-based image retrieval from two aspects: (1) irrelevant visual contents (e.g.
background) scatter the mapping from image to human perception; (2) unsupervised feature extraction and similarity ranking
method can not accurately reveal users’ image perception. This paper proposes a novel region-based retrieval framework—dynamic
region matching (DRM) to bridge the semantic gap. (1) To address the first issue, a probabilistic fuzzy region matching algorithm
is adopted to retrieve and match images precisely at object level, which copes with the problem of inaccurate segmentation.
(2) To address the second issue, a “FeatureBoost” algorithm is proposed to construct an effective “eigen” feature set in relevance
feedback (RF) process. And the significance of each region is dynamically updated in RF learning to automatically capture
users’ region of interest (ROI). (3) User’s retrieval purpose is predicted using a novel log-learning algorithm, which predicts
users’ retrieval target in the feature space using the accumulated user operations. Extensive experiments have been conducted
on Corel image database with over 10,000 images. The promising experimental results reveal the effectiveness of our scheme
in bridging the semantic gap. 相似文献
9.
基于支持向量机(SupportVectorMachine,SVM)理论的相关反馈技术是可有效提高图像检索性能的重要手段之一。然而,大多数SVM反馈算法普遍受到小样本问题的制约。本文综合了集成学习、半监督学习和主动学习三种方法的技术特点,提出一种混合学习框架下的SVM反馈算法。该算法在Boosting迭代过程中使用了未标记图像,以增加个体SVM之间的差异,从而获得高效的集成学习模型。同时,高效的集成学习模型更有利于寻找富有信息(most informative)图像,从而也提高了用户主动反馈的效率。实验结果及对比分析表明,混合学习策略可有效改进相关反馈的性能。 相似文献