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检索条件"主题词=Content-based Image retrieval"

共 1767 条记录，以下是61-70 订阅

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content-based image retrieval using shifted histogram

Content-based image retrieval using shifted histogram

引用

7th International Conference on Computational Science (ICCS 2007)

作者： Yoo, Gi-Hyoung Kim, Beob Kyun You, Kang Soo Chonbuk Natl Univ Dept Comp Engn Jeonju 561756 South Korea Korea Inst Sci & Technol Informat Daejeon 350806 South Korea Jeonju Univ Sch Liberal Arts Jeonju 561756 South Korea

ISBN: (纸本)9783540725879

This paper proposes the shifted histogram method (SHM), for histogram-based image retrieval based on the dominant colors in images. The histogram-based method is very suitable for color image retrieval because retrievals are unaffected by geometrical changes in images, such as translation and rotation. images with the same visual information, but with shifted color intensity, may significantly degrade if the conventional histogram intersection method (HIM) is used. In order to solve this problem, we propose the shifted histogram method (SHM). Our experimental results show that the shifted histogram method

关键词： content-based image retrieval color histogram shift histogram

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content-based image retrieval using Convolutional Neural Networks

Content-Based Image Retrieval using Convolutional Neural Net...

引用

IEEE International Conference on Signals and Systems (ICSigSys)

作者： Rian, Zakhayu Christanti, Viny Hendryli, Janson Tarumangara Univ Fac Informat Technol Jakarta Indonesia

ISBN: (纸本)9781728121772

Searching a collection of images that have similarities with input images, without knowing the name of the image, makes a search system that applies the concept of content-based image retrieval (CBIR), is very necessary. In general, CBIR systems use visual features such as color, image edge, texture, and suitability of names in input images with images in the database. The method for classification is convolutional neural networks (CNN), while retrieval with cosine similarity. Dataset is divided into 5 masterclasses, each masterclass has 5 subclasses. The class used for retrieval is a masterclass, where the images of each large class are combined images of subclasses in the large class. From the experiments, we found that the CNN method has succeeded in supporting the retrieval task, by classifying image classes.

关键词： cosine similarity content-based image retrieval convolutional neural networks deep learning VGG16

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content-based image retrieval Using Color Difference Histogram in image Textures 5

Content-based Image Retrieval Using Color Difference Histogr...

引用

5th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS)

作者： Ajam, Armin Forghani, Majid AlyanNezhadi, Mohammad M. Qazanfari, Hamed Amiri, Zahra Shahrood Nonprofit & Nongovt Higher Edu Inst Dept Comp Engn Shahrood Iran Ural Fed Univ Inst Nat Sci & Math Ekaterinburg Russia Shahrood Univ Technol Image Proc & Data Min Lab Shahrood Iran

ISBN: (纸本)9781728153506

The aim of content-based image retrieval system is finding similar images to the query image from a database based on its visual content. In this paper, a novel retrieval system based on human vision is proposed. A factor that has a high impact on the search process is a set of features which are used in. The recent studies emerged that the human eye system considers the image content, texture, and color properties more than other features. Therefore, to retrieve more precisely the images, features should be used that are close to the human eye system. In the current paper, at first, the texture is extracted from the images using the local binary patterns algorithm. After that, the color differences of two adjacent pixels with the same texture are calculated in the HSV color space. Afterward, the histogram is taken from the color difference values. The obtained features from the histogram can describe the visual content of the images in more detail. Finally, the effective features are selected based on their entropy value. The prominent advantage of the proposed method is the lack of implementation of segmentation, clustering, training, and any other method of machine learning, which requires a lot of processing and time. The method is evaluated on two standard Corel 10K and Corel 5K databases, and its retrieval rate is significantly improved compared to some recent methods.

关键词： content-based image retrieval Color Difference LBP Texture Color Difference Histogram Entropy

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content-based image retrieval using Scale Invariant Feature Transform and Gray Level Cooccurrence Matrix 2

Content-Based Image Retrieval using Scale Invariant Feature ...

引用

2nd International Workshop on Pattern Recognition

作者： Srivastava, Prashant Khare, Manish Khare, Ashish Univ Allahabad Dept Elect & Commun Allahabad Uttar Pradesh India GIST Sch Informat & Commun Gwangju 500712 South Korea

ISBN: (数字)9781510613515

ISBN: (纸本)9781510613508;9781510613515

The rapid growth of different types of images has posed a great challenge to the scientific fraternity. As the images are increasing everyday, it is becoming a challenging task to organize the images for efficient and easy access. The field of image retrieval attempts to solve this problem through various techniques. This paper proposes a novel technique of image retrieval by combining Scale Invariant Feature Transform (SIFT) and Co-occurrence matrix. For construction of feature vector, SIFT descriptors of gray scale images are computed and normalized using z-score normalization followed by construction of Gray-Level Cooccurrence Matrix (GLCM) of normalized SIFT keypoints. The constructed feature vector is matched with those of images in database to retrieve visually similar images. The proposed method is tested on Corel-1K dataset and the performance is measured in terms of precision and recall. The experimental results demonstrate that the proposed method outperforms some of the other state-of-the-art methods.

关键词： content-based image retrieval Scale Invariant Feature Transform Gray-Level Cooccurrence Matrix

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content-based image retrieval in the Topic Space using SOM and LDA 3

Content-Based Image Retrieval in the Topic Space using SOM a...

引用

INTERNATIONAL CONFERENCE ON CONTROL ENGINEERING & INFORMATION TECHNOLOGY (CEIT)

作者： Ayech, Marouane Ben Haj Amiri, Hamid Univ Tunis El Manar Natl Sch Engn Tunis Tunis Tunisia

ISBN: (纸本)9781479982134

content-based image retrieval (CBIR) becomes necessary when dealing with large collections of images. Recently, interesting retrieval methods are based on Bag-of-Words (BoW) model. It allows the projection of an image in a vector space called the word space. Nevertheless, this space can be further refined in order to reflect the semantic content of an image. Indeed, it can be transformed into a lower dimensional vector space called the topic space. In this paper, we propose a retrieval system based on an unsupervised learning: First, a Self-Organizing Map (SOM) is learnt to construct the word space from the feature space. Then, the word space is transformed into the topic space using the Latent Dirichlet Allocation (LDA) model. Once learnt, our model allows to infer a compact and semantic image representation. Experiments are performed using a set of "vehicule" images from Pascal VOC 2007 dataset. Evaluation shows that the proposed retrieval system leads to encouraging results.

关键词： Bag-of-Words Latent Dirichlet Allocation content-based image retrieval Self Organizing Map Topic space

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content-based image retrieval using a Gaussian mixture model in the wavelet domain

Content-based image retrieval using a Gaussian mixture model...

引用

Conference on Visual Communications and image Processing 2003

作者： Yuan, H Zhang, XP Guan, L Ryerson Univ Dept Elect & Comp Engn Toronto ON M5B 2K3 Canada

ISBN: (纸本)0819450235

The research on content-based image retrieval (CBIR) has been very active in recent years. The performance of a CBIR system can be significantly improved by selecting a good indexing feature space to represent image characteristics. In this paper, we introduce a statistical-model based technique for analyzing and extracting image features in the wavelet domain. The images are decomposed into a set of wavelet subspaces in the wavelet domain and for each wavelet subspace, a two component Gaussian mixture model is developed to describe the statistical characteristics of the wavelet coefficients. The model parameters, which are a good reflection of image features in the wavelet subspaces, are obtained by an EM (Expectation-Maximization) algorithm and employed to construct the indexing feature space for a CBIR system. We apply the new method on the Brodatz image database to demonstrate its performance. The experimental results indicate that our indexing feature space is very effective in representing image characteristics and provides a high retrieval rate in the CBIR system. When compared with some other conventional feature extraction methods, the new method achieves comparable retrieval performance with less number of features in the feature space, which means it is more computationally efficient.

关键词： content-based image retrieval feature extraction wavelet transforms Gaussian mixture model EM algorithm

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content-based image retrieval Using Local Texture-based Color Histogram 2

Content-based Image Retrieval Using Local Texture-Based Colo...

引用

IEEE 2nd International Conference on Cybernetics (CYBCONF)

作者： Nan, Bingfei Xu, Ye Mu, Zhichun Chen, Long Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing Peoples R China Jiangxi Univ Sci & Technol Sch Informat Engn Ganzhou Jiangxi Peoples R China

ISBN: (纸本)9781479983223

this paper presents a novel image feature representation method, called local texture-based color histogram (LTCH), for content-based image retrieval. The LTCH can describe the color distribution under a mask, which is defined as a micro-structure image with a near-uniform texture. The near-uniform texture is exacted by center symmetric local trinary pattern (CS-LTP) and micro-structure map. The CS-LTP is coding on a quantized HSV image, and the micro-structure map is defined with the same as CS-LTP code. The LTCH can be considered as a novel visual attribute descriptor combining local texture, color and spatial layout, without any image segmentation and model training. The proposed LTCH method is evaluated on Corel-1000 database and Corel-5000 database with the standard performance evaluation method, for image retrieval. The experimental results demonstrate that the proposed method has a better performance than representative image feature descriptors, such as color difference histogram (CDH), microstructure descriptor (MSD), multi-texton histogram (MTH) and structure elements' descriptor (SED).

关键词： near-uniform texture center symmetric local trinary pattern micro-structure map color histogram content-based image retrieval

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content-based image retrieval as a computer aid for the detection of mammographic masses

Content-based image retrieval as a computer aid for the dete...

引用

Medical Imaging 2003 Conference

作者： Tourassi, GD Vargas-Voracek, R Floyd, CE Duke Univ Med Ctr Dept Radiol Durham NC 27710 USA

ISBN: (纸本)0819448338

The purpose of the study was to develop and evaluate a content-based image retrieval (CBIR) approach as a computer aid for the detection of masses in screening mammograms. The study was based on the Digital Database for Screening Mammography (DDSM). Initially, a knowledge database of 1,009 mammographic regions was created. They were all 512x512 pixel ROIs with known pathology. Specifically, there were 340 ROIs depicting a biopsy-proven malignant mass, 341 ROIs with a benign mass, and the remaining 328 ROIs were normal. Subsequently, the CBIR algorithm was implemented using mutual information (MI) as the similarity metric for image retrieval. The CBIR algorithm formed the basis of a knowledge-based CAD system. The system operated as follows. Given a databank of mammographic regions with known pathology, a query suspicious mammographic region was evaluated. based on their information content, all similar cases in the databank were retrieved. The matches were rank-ordered and a decision index was calculated using the query's best matches. based on a leave-one out sampling scheme, the CBIR-CAD system achieved an ROC area index A(Z) = 0.87 +/- 0.01 and a partial ROC area index (0.90)A(Z) = 0.45 +/- 0.03 for the detection of masses in screening mammograms.

关键词： content-based image retrieval mammography computer-assisted detection mutual information

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content-based image retrieval On Reconfigurable Peer-To-Peer Networks

Content-Based Image Retrieval On Reconfigurable Peer-To-Peer...

引用

14th IEEE International Workshop on Multimedia Signal Processing (MMSP)

作者： Su, Chun-Rong Chen, Jiann-Jone Natl Taiwan Univ Sci & Technol Dept Elect Engn Taipei 10673 Taiwan

ISBN: (纸本)9781467345729

Performing content-based image retrieval (CBIR) from Internet databases connected through Peer-to-Peer (P2P) network, abbreviated as P2P-CBIR, helps to effectively explore the large-scale image database distributed over connected peers. Decentralized unstructured P2P framework is adopted in our system to compromise with the structured one while still reserving flexible routing control when peers join/leave or network fails. The P2P-CBIR search engine is designed to provide multi-instance query with multi-feature types to effectively reduce network traffic and maintain high retrieval accuracy. The proposed P2P-CBIR system is also designed to provide scalable retrieval control, which can adaptively control the query scope and progressively refine the accuracy of retrieved results. We also proposed to provide the most updated local database characteristics for the P2P-CBIR users. By reconfiguring system at each regular interval time, it can effectively reduce trivial peer routing and retrieval operations due to imprecise configuration. Experiments demonstrated that the average recall rate of the proposed P2P-CBIR with reconfiguration is higher than the one without about 20%, and the latter outperforms previous methods, i.e., firework query model (FQM) and breadth-first search (BFS) about 20% and 120%, respectively, under the same range of TTL values.

关键词： content-based image retrieval retrieval recall peer to peer file sharing Peers image retrieval visual databases Peer-to-peer network Time intervals traffic network Reconfiguration retrieval configurability

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content-based image retrieval on Reconfigurable Peer-to-Peer Networks

Content-Based Image Retrieval on Reconfigurable Peer-to-Peer...

引用

International Symposium on Biometrics and Security Technologies (ISBAST)

作者： Su, Chun-Rong Chen, Jiann-Jone Chang, Kai-Lin Natl Taiwan Univ Sci & Technol Dept Elect Engn Taipei 10673 Taiwan

ISBN: (纸本)9780769550107

Performing content-based image retrieval (CBIR) from Internet databases connected through Peer-to-Peer (P2P) network, abbreviated as P2P-CBIR, helps to effectively explore the large-scale image database distributed over connected peers. Decentralized unstructured P2P framework is adopted in our system to compromise with the structured one while still reserving flexible routing control when peers join/leave or network fails. The P2P-CBIR search engine is designed to provide multi-instance query with multi-feature types to effectively reduce network traffic and maintain high retrieval accuracy. The proposed P2P-CBIR system is also designed to provide scalable retrieval control, which can adaptively control the query scope and progressively refine the accuracy of retrieved results. We also proposed to provide the most updated local database characteristics for the P2P-CBIR users. By reconfiguring system at each regular interval time, we can effectively reduce trivial peer routing and retrieval operations due to imprecise configuration. Experiments demonstrated that the average recall rate of the proposed P2P-CBIR with reconfiguration is higher than that of the one without about 10.73%, and the latter outperforms previous methods, i.e., firework query model (FQM) and breadth-first search (BFS) about 27% and 57%, respectively, under the same range of TTL values.

关键词： peer-to-peer networks multi-instance query content-based image retrieval scalable retrieval network search reconfiguration

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