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《Journal of Visual Communication and Image Representation》2014,25(5):963-969
Efficient multimedia retrieval has become a vital issue because more audio and video data are now available. This paper focuses on content-based image retrieval (CBIR) in the compression domain (CPD). The retrieval features are extracted based on I-frame coding information in H.264. This paper proposes using a local mode histogram as the texture feature to match images and applying the residual coefficients to filter non-confident modes. The geometrical correspondence between two images is also considered. The experimental results show that the proposed method can substantially reduce computational and memory resource consumption, and provides similar performance compared with methods that extract features from decompressed images. 相似文献
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基于多尺度相位特征的图像检索方法 总被引:1,自引:0,他引:1
在基于内容的图像检索中,一个关键的问题是图像视觉内容的表述。而传统的颜色,形状和纹理特征对于图像内容的表述尚且不够完备。为进一步提高检索准确率,针对人眼视觉特性,该文提出了一种基于多尺度相位特征的图像检索方法。该方法首先采用尺度空间理论得到图像的多尺度描述,然后通过复数可调滤波(complex steerable filtering)提取图像的多尺度相位信息并利用直方图投影获取全局统计的多尺度相位特征。在通用数据库COREL 5000上的实验结果表明,该特征相对经典的颜色特征提高至少5%检索准确率,且能对之提供有效补充。 相似文献
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基于底层视觉特征和先验知识的显著性区域检测算法难以检测一些复杂的显著性目标,人的视觉系统能分辨这些目标是由于其中包含丰富的语义知识.本文构建了一个基于全卷积结构的语义显著性区域检测网络,用数据驱动的方式构建从图像底层特征到人类语义认知的映射,提取语义显著性区域.针对网络提取的语义显著性区域的缺点,本文进一步引入颜色信息、目标边界信息、空间一致性信息获得准确的超像素级前景和背景概率.最后提出一个优化模型融合前景和背景概率信息、语义信息、空间一致性信息得到最终的显著性区域图.在6个数据集上与15种最新算法的比较实验证明了本文算法的有效性和鲁棒性. 相似文献
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Jinman Kim Weidong Cai Dagan Feng Hao Wu 《IEEE transactions on information technology in biomedicine》2006,10(3):598-607
The advances in digital medical imaging and storage in integrated databases are resulting in growing demands for efficient image retrieval and management. Content-based image retrieval (CBIR) refers to the retrieval of images from a database, using the visual features derived from the information in the image, and has become an attractive approach to managing large medical image archives. In conventional CBIR systems for medical images, images are often segmented into regions which are used to derive two-dimensional visual features for region-based queries. Although such approach has the advantage of including only relevant regions in the formulation of a query, medical images that are inherently multidimensional can potentially benefit from the multidimensional feature extraction which could open up new opportunities in visual feature extraction and retrieval. In this study, we present a volume of interest (VOI) based content-based retrieval of four-dimensional (three spatial and one temporal) dynamic PET images. By segmenting the images into VOIs consisting of functionally similar voxels (e.g., a tumor structure), multidimensional visual and functional features were extracted and used as region-based query features. A prototype VOI-based functional image retrieval system (VOI-FIRS) has been designed to demonstrate the proposed multidimensional feature extraction and retrieval. Experimental results show that the proposed system allows for the retrieval of related images that constitute similar visual and functional VOI features, and can find potential applications in medical data management, such as to aid in education, diagnosis, and statistical analysis. 相似文献
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《Journal of Visual Communication and Image Representation》2014,25(6):1308-1323
Content-based image retrieval (CBIR) has been an active research topic in the last decade. As one of the promising approaches, salient point based image retrieval has attracted many researchers. However, the related work is usually very time consuming, and some salient points always may not represent the most interesting subset of points for image indexing. Based on fast and performant salient point detector, and the salient point expansion, a novel content-based image retrieval using local visual attention feature is proposed in this paper. Firstly, the salient image points are extracted by using the fast and performant SURF (Speeded-Up Robust Features) detector. Then, the visually significant image points around salient points can be obtained according to the salient point expansion. Finally, the local visual attention feature of visually significant image points, including the weighted color histogram and spatial distribution entropy, are extracted, and the similarity between color images is computed by using the local visual attention feature. Experimental results, including comparisons with the state-of-the-art retrieval systems, demonstrate the effectiveness of our proposal. 相似文献
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Local maximum edge binary patterns: A new descriptor for image retrieval and object tracking 总被引:1,自引:0,他引:1
A new algorithm meant for content based image retrieval (CBIR) and object tracking applications is presented in this paper. The local region of image is represented by local maximum edge binary patterns (LMEBP), which are evaluated by taking into consideration the magnitude of local difference between the center pixel and its neighbors. This LMEBP differs from the existing LBP in a manner that it extracts the information based on distribution of edges in an image. Further, the effectiveness of our algorithm is confirmed by combining it with Gabor transform. Four experiments have been carried out for proving the worth of our algorithm. Out of which three are meant for CBIR and one for object tracking. It is further mentioned that the database considered for first three experiments are Brodatz texture database (DB1), MIT VisTex database (DB2), rotated Brodatz database (DB3) and the fourth contains three observations. The results after being investigated show a significant improvement in terms of their evaluation measures as compared to LBP and other existing transform domain techniques. 相似文献
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Relevance feedback has proven to be a powerful tool to bridge the semantic gap between low-level features and high-level human concepts in content-based image retrieval (CBIR). However, traditional short-term relevance feedback technologies are confined to using the current feedback record only. Log-based long-term learning captures the semantic relationships among images in a database by analyzing the historical relevance information to boost the retrieval performance effectively. In this paper, we propose an expanded-judging model to analyze the historical log data’s semantic information and to expand the feedback sample set from both positive and negative relevant information. The index table is used to facilitate the log analysis. The expanded-judging model is applied in image retrieval by combining with short-term relevance feedback algorithms. Experiments were carried out to evaluate the proposed algorithm based on the Corel image database. The promising experimental results validate the effectiveness of our proposed expanded-judging model. 相似文献
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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
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With many potential practical applications, content-based image retrieval (CBIR) has attracted substantial attention during the past few years. A variety of relevance feedback (RF) schemes have been developed as a powerful tool to bridge the semantic gap between low-level visual features and high-level semantic concepts, and thus to improve the performance of CBIR systems. Among various RF approaches, support-vector-machine (SVM)-based RF is one of the most popular techniques in CBIR. Despite the success, directly using SVM as an RF scheme has two main drawbacks. First, it treats the positive and negative feedbacks equally, which is not appropriate since the two groups of training feedbacks have distinct properties. Second, most of the SVM-based RF techniques do not take into account the unlabeled samples, although they are very helpful in constructing a good classifier. To explore solutions to overcome these two drawbacks, in this paper, we propose a biased maximum margin analysis (BMMA) and a semisupervised BMMA (SemiBMMA) for integrating the distinct properties of feedbacks and utilizing the information of unlabeled samples for SVM-based RF schemes. The BMMA differentiates positive feedbacks from negative ones based on local analysis, whereas the SemiBMMA can effectively integrate information of unlabeled samples by introducing a Laplacian regularizer to the BMMA. We formally formulate this problem into a general subspace learning task and then propose an automatic approach of determining the dimensionality of the embedded subspace for RF. Extensive experiments on a large real-world image database demonstrate that the proposed scheme combined with the SVM RF can significantly improve the performance of CBIR systems. 相似文献
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Andre B Vercauteren T Buchner AM Wallace MB Ayache N 《IEEE transactions on medical imaging》2012,31(6):1276-1288
Content-based image retrieval (CBIR) is a valuable computer vision technique which is increasingly being applied in the medical community for diagnosis support. However, traditional CBIR systems only deliver visual outputs, i.e., images having a similar appearance to the query, which is not directly interpretable by the physicians. Our objective is to provide a system for endomicroscopy video retrieval which delivers both visual and semantic outputs that are consistent with each other. In a previous study, we developed an adapted bag-of-visual-words method for endomicroscopy retrieval, called "Dense-Sift," that computes a visual signature for each video. In this paper, we present a novel approach to complement visual similarity learning with semantic knowledge extraction, in the field of in vivo endomicroscopy. We first leverage a semantic ground truth based on eight binary concepts, in order to transform these visual signatures into semantic signatures that reflect how much the presence of each semantic concept is expressed by the visual words describing the videos. Using cross-validation, we demonstrate that, in terms of semantic detection, our intuitive Fisher-based method transforming visual-word histograms into semantic estimations outperforms support vector machine (SVM) methods with statistical significance. In a second step, we propose to improve retrieval relevance by learning an adjusted similarity distance from a perceived similarity ground truth. As a result, our distance learning method allows to statistically improve the correlation with the perceived similarity. We also demonstrate that, in terms of perceived similarity, the recall performance of the semantic signatures is close to that of visual signatures and significantly better than those of several state-of-the-art CBIR methods. The semantic signatures are thus able to communicate high-level medical knowledge while being consistent with the low-level visual signatures and much shorter than them. In our resulting retrieval system, we decide to use visual signatures for perceived similarity learning and retrieval, and semantic signatures for the output of an additional information, expressed in the endoscopist own language, which provides a relevant semantic translation of the visual retrieval outputs. 相似文献
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Finding an image from a large set of images is an extremely difficult problem. One solution is to label images manually, but this is very expensive, time consuming and infeasible for many applications. Furthermore, the labeling process depends on the semantic accuracy in describing the image. Therefore many Content based Image Retrieval (CBIR) systems are developed to extract low-level features for describing the image content. However, this approach decreases the human interaction with the system due to the semantic gap between low-level features and high-level concepts. In this study we make use of fuzzy logic to improve CBIR by allowing users to express their requirements in words, the natural way of human communication. In our system the image is represented by a Fuzzy Attributed Relational Graph (FARG) that describes each object in the image, its attributes and spatial relation. The texture and color attributes are computed in a way that model the Human Vision System (HSV). We proposed a new approach for graph matching that resemble the human thinking process. The proposed system is evaluated by different users with different perspectives and is found to match users’ satisfaction to a high degree. 相似文献
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Image quality assessment (IQA) is a useful technique in computer vision and machine intelligence. It is widely applied in image retrieval, image clustering and image recognition. IQA algorithms generally rely on human visual system (HVS), which can reflect how human perceive salient regions in the image. In this paper, we leverage both low-level features and high-level semantic features to select salient regions, which will be concatenated to form GSPs by the designed saliency-constraint algorithm to mimic human visual system. We design an enhanced IQA index based on the GSPs to calculate the simialrity between reference image and test image to achieve image quality assessment. Experiments demonstrate that our IQA method can achieve satisfactory performance. 相似文献
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In order to improve the retrieval performance of images, this paper proposes an efficient approach for extracting and retrieving color images. The block diagram of our proposed approach to content-based image retrieval (CBIR) is given firstly, and then we introduce three image feature extracting arithmetic including color histogram, edge histogram and edge direction histogram, the histogram Euclidean distance, cosine distance and histogram intersection are used to measure the image level similarity. On the basis of using color and texture features separately, a new method for image retrieval using combined features is proposed. With the test for an image database including 766 general-purpose images and comparison and analysis of performance evaluation for features and similarity measures, our proposed retrieval approach demonstrates a promising performance. Experiment shows that combined features are superior to every single one of the three features in retrieval. 相似文献