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《Journal of Visual Communication and Image Representation》2014,25(6):1324-1334
A new image indexing and retrieval algorithm for content based image retrieval is proposed in this paper. The local region of the image is represented by making the use of local difference operator (LDO), separating it into two components i.e. sign and magnitude. The sign LBP operator (S_LBP) is a generalized LBP operator. The magnitude LBP (M_LBP) operator is calculated using the magnitude of LDO. A robust LBP (RLBP) operator is presented employing robust S_LBP and robust M_LBP. Further, the combination of Gabor transform and RLBP operator has also been presented. The robustness is established by conducting four experiments on different image database i.e. Corel 1000 (DB1), Brodatz texture database (DB2) and MIT VisTex database (DB3) under different lighting (illumination) and noise conditions. Investigations reveal a promising achievement of the technique presented when compared to S_LBP and other existing transform domain techniques in terms of their evaluation measures. 相似文献
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基于内容的图像检索技术研究 总被引:1,自引:0,他引:1
随着互联网技术的快速发展,传统的基于关键字的图像检索已无法满足人们的需要,基于内容的图像检索技术(CBIR)越来越受到人们的青睐.现阐述了基于内容的图像检索系统的组成和基本原理,并着重介绍了CBIR的特征提取,相关反馈的关键技术,最后指出了基于内容的图像检索存在的问题和发展方向. 相似文献
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In this article, we propose a novel system for feature selection, which is one of the key problems in content-based image
indexing and retrieval as well as various other research fields such as pattern classification and genomic data analysis.
The proposed system aims at enhancing semantic image retrieval results, decreasing retrieval process complexity, and improving
the overall system usability for end-users of multimedia search engines. Three feature selection criteria and a decision method
construct the feature selection system. Two novel feature selection criteria based on inner-cluster and intercluster relations
are proposed in the article. A majority voting-based method is adapted for efficient selection of features and feature combinations.
The performance of the proposed criteria is assessed over a large image database and a number of features, and is compared
against competing techniques from the literature. Experiments show that the proposed feature selection system improves semantic
performance results in image retrieval systems.
This work was supported by the Academy of Finland, Project No. 213,462 (Finnish Centre of Excellence Program 2006–2011). 相似文献
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Embedded colour image coding for content-based retrieval 总被引:1,自引:0,他引:1
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In this paper, a novel study on system profiles and adaptation of parameters for end-users of content-based indexing and retrieval
(CBIR) applications are presented. The main objective of the study is improving the overall CBIR application performance in
different hardware platforms having different technical capabilities and conditions. We define CBIR system profiles in terms
of hardware and system platform attributes and propose CBIR parameters for each profile. Hence, the study consists of two
main parts: system profiling and adaptation of indexing and retrieval parameters for each profile. The proposed CBIR parameters
are appropriate configurations for optimal CBIR use on every platform. The proposed parameters for each system profile are
assessed over a large set of experiments. Experimental studies show that the proposed parameters for each system profile have
satisfactory semantic retrieval performance, with reduced computational complexity and storage space requirement. 45 to 78%
improvement is achieved in the computational complexity of the retrieval process depending on the profile. 相似文献
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Learning effective relevance measures plays a crucial role in improving the performance of content-based image retrieval (CBIR) systems. Despite extensive research efforts for decades, how to discover and incorporate semantic information of images still poses a formidable challenge to real-world CBIR systems. In this paper, we propose a novel hybrid textual-visual relevance learning method, which mines textual relevance from image tags and combines textual relevance and visual relevance for CBIR. To alleviate the sparsity and unreliability of tags, we first perform tag completion to fill the missing tags as well as correct noisy tags of images. Then, we capture users’ semantic cognition to images by representing each image as a probability distribution over the permutations of tags. Finally, instead of early fusion, a ranking aggregation strategy is adopted to sew up textual relevance and visual relevance seamlessly. Extensive experiments on two benchmark datasets well verified the promise of our approach. 相似文献
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The complexity of multimedia contents is significantly increasing in the current digital world. This yields an exigent demand for developing highly effective retrieval systems to satisfy human needs. Recently, extensive research efforts have been presented and conducted in the field of content-based image retrieval (CBIR). The majority of these efforts have been concentrated on reducing the semantic gap that exists between low-level image features represented by digital machines and the profusion of high-level human perception used to perceive images. Based on the growing research in the recent years, this paper provides a comprehensive review on the state-of-the-art in the field of CBIR. Additionally, this study presents a detailed overview of the CBIR framework and improvements achieved; including image preprocessing, feature extraction and indexing, system learning, benchmarking datasets, similarity matching, relevance feedback, performance evaluation, and visualization. Finally, promising research trends, challenges, and our insights are provided to inspire further research efforts. 相似文献
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This paper proposes a new algorithm using global and local features for content-based image retrieval. Global features are extracted using the magnitude of Zernike moments (ZMs). Local features are obtained through local directional pattern (LDP). Generally, LDP is used to extract texture-based features from an image. In this paper, LDP is used to encode both texture and shape information of an image to represent more meaningful features. To encode texture-based features, original image is used to compute the LDP features. To extract shape information from an image, dual-tree complex wavelet transform (DT-CWT) is applied on image which generates six directional wavelets. These six directional wavelets are superimposed in order to obtain shape-encoded image. LDP is then applied on this wavelet-based shape-encoded image. Further, to enhance retrieval accuracy, LDP features are extracted from patches of both original and shape-encoded images. These patches are assigned with weights based on average discrimination capability of features in a patch. Experiments are performed using three different standard databases with various variations such as pose, distortion, partial occlusion and complex structure. The proposed technique achieves 96.4 and 98.76 % retrieval accuracy at a recall of 50 %, for Kimia-99 and COIL-100 databases, respectively. For MPEG-7 CE-2 shape database, retrieval accuracy of 61.93 % is achieved in terms of average Bull’s eye performance (BEP). The proposed technique is also tested on Springer medical image database to explore its scope in other areas, wherein it attains average BEP of 69.68 % in comparison with 61.52 % with ZMs. It is observed that the proposed technique outperforms other well-known existing methods of image retrieval. 相似文献
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Guoping Qiu Kin-Man Lam 《IEEE transactions on image processing》2003,12(1):102-113
Image patches of different spatial frequencies are likely to have different perceptual significance as well as reflect different physical properties. Incorporating such concept is helpful to the development of more effective image retrieval techniques. We introduce a method which separates an image into layers, each of which retains only pixels in areas with similar spatial frequency characteristics and uses simple low-level features to index the layers individually. The scheme associates indexing features with perceptual and physical significance thus implicitly incorporating high level knowledge into low level features. We present a computationally efficient implementation of the method, which enhances the power and at the same time retains the simplicity and elegance of basic color indexing. Experimental results are presented to demonstrate the effectiveness of the method. 相似文献
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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
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In order to protect data privacy, image with sensitive or private information needs to be encrypted before being outsourced to a cloud service provider. However, this causes difficulties in image retrieval and data management. A privacy-preserving content-based image retrieval method based on orthogonal decomposition is proposed in the paper. The image is divided into two different components, for which encryption and feature extraction are executed separately. As a result, cloud server can extract features from an encrypted image directly and compare them with the features of the queried images, so that users can thus obtain the image. Different from other methods, the proposed method has no special requirements to encryption algorithms, which makes it more universal and can be applied in different scenarios. Experimental results prove that the proposed method can achieve better security and better retrieval performance. 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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基于内容的图像检索在病虫害管理中的应用 总被引:3,自引:0,他引:3
综述了我国目前森林病虫害管理的现状以及存在的不足,提出了将基于内容的图像检索技术应用在森林病虫害管理的新思路。然后分析了基于内客的图像检索技术的特点,森林病虫害管理的体系结构、主要技术的研究情况,以及基于内容的图像检索技术在森林病虫害管理中应用的重大意义。 相似文献
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Murala S Maheshwari RP Balasubramanian R 《IEEE transactions on image processing》2012,21(5):2874-2886
In this paper, we propose a novel image indexing and retrieval algorithm using local tetra patterns (LTrPs) for content-based image retrieval (CBIR). The standard local binary pattern (LBP) and local ternary pattern (LTP) encode the relationship between the referenced pixel and its surrounding neighbors by computing gray-level difference. The proposed method encodes the relationship between the referenced pixel and its neighbors, based on the directions that are calculated using the first-order derivatives in vertical and horizontal directions. In addition, we propose a generic strategy to compute nth-order LTrP using (n - 1)th-order horizontal and vertical derivatives for efficient CBIR and analyze the effectiveness of our proposed algorithm by combining it with the Gabor transform. The performance of the proposed method is compared with the LBP, the local derivative patterns, and the LTP based on the results obtained using benchmark image databases viz., Corel 1000 database (DB1), Brodatz texture database (DB2), and MIT VisTex database (DB3). Performance analysis shows that the proposed method improves the retrieval result from 70.34%/44.9% to 75.9%/48.7% in terms of average precision/average recall on database DB1, and from 79.97% to 85.30% and 82.23% to 90.02% in terms of average retrieval rate on databases DB2 and DB3, respectively, as compared with the standard LBP. 相似文献