Image retrieval

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An image retrieval system is a computer system for browsing, searching and retrieving images from a large database of digital images. (Wikipedia.org)






Conferences related to Image retrieval

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2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted papers will be peer reviewed. Accepted high quality papers will be presented in oral and postersessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE


2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI 2020)

The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2020 will be the 17th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2020 meeting will continue this tradition of fostering cross-fertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging.ISBI 2019 will be the 16th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2019 meeting will continue this tradition of fostering cross fertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2018 will be the 15th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2018 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2017 will be the 14th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2017 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI 2016)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forumfor the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2016 willbe the thirteenth meeting in this series. The previous meetings have played a leading role in facilitatinginteraction between researchers in medical and biological imaging. The 2016 meeting will continue thistradition of fostering crossfertilization among different imaging communities and contributing to an integrativeapproach to biomedical imaging across all scales of observation.

  • 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI 2015)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2015 will be the 12th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2014 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI 2014)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2014 will be the eleventh meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2014 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI 2013)

    To serve the biological, biomedical, bioengineering, bioimaging and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2012 IEEE 9th International Symposium on Biomedical Imaging (ISBI 2012)

    To serve the biological, biomedical, bioengineering, bioimaging, and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2011 IEEE 8th International Symposium on Biomedical Imaging (ISBI 2011)

    To serve the biological, biomedical, bioengineering, bioimaging, and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2010 IEEE 7th International Symposium on Biomedical Imaging (ISBI 2010)

    To serve the biological, biomedical, bioengineering, bioimaging, and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2009 IEEE 6th International Symposium on Biomedical Imaging (ISBI 2009)

    Algorithmic, mathematical and computational aspects of biomedical imaging, from nano- to macroscale. Topics of interest include image formation and reconstruction, computational and statistical image processing and analysis, dynamic imaging, visualization, image quality assessment, and physical, biological and statistical modeling. Molecular, cellular, anatomical and functional imaging modalities and applications.

  • 2008 IEEE 5th International Symposium on Biomedical Imaging (ISBI 2008)

    Algorithmic, mathematical and computational aspects of biomedical imaging, from nano- to macroscale. Topics of interest include image formation and reconstruction, computational and statistical image processing and analysis, dynamic imaging, visualization, image quality assessment, and physical, biological and statistical modeling. Molecular, cellular, anatomical and functional imaging modalities and applications.

  • 2007 IEEE 4th International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2007)

  • 2006 IEEE 3rd International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2006)

  • 2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2004)

  • 2002 1st IEEE International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2002)


2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.

  • 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premier annual computer vision event comprising the main conference and severalco-located workshops and short courses. With its high quality and low cost, it provides anexceptional value for students, academics and industry researchers.

  • 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.

  • 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conferenceand 27co-located workshops and short courses. With its high quality and low cost, it provides anexceptional value for students,academics and industry.

  • 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry.

  • 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    computer, vision, pattern, cvpr, machine, learning

  • 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. Main conference plus 50 workshop only attendees and approximately 50 exhibitors and volunteers.

  • 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry.

  • 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Topics of interest include all aspects of computer vision and pattern recognition including motion and tracking,stereo, object recognition, object detection, color detection plus many more

  • 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Sensors Early and Biologically-Biologically-inspired Vision, Color and Texture, Segmentation and Grouping, Computational Photography and Video

  • 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Concerned with all aspects of computer vision and pattern recognition. Issues of interest include pattern, analysis, image, and video libraries, vision and graphics, motion analysis and physics-based vision.

  • 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Concerned with all aspects of computer vision and pattern recognition. Issues of interest include pattern, analysis, image, and video libraries, vision and graphics,motion analysis and physics-based vision.

  • 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2007 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2006 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2005 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)


2020 IEEE International Conference on Image Processing (ICIP)

The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.


2020 IEEE International Symposium on Circuits and Systems (ISCAS)

The International Symposium on Circuits and Systems (ISCAS) is the flagship conference of the IEEE Circuits and Systems (CAS) Society and the world’s premier networking and exchange forum for researchers in the highly active fields of theory, design and implementation of circuits and systems. ISCAS2020 focuses on the deployment of CASS knowledge towards Society Grand Challenges and highlights the strong foundation in methodology and the integration of multidisciplinary approaches which are the distinctive features of CAS contributions. The worldwide CAS community is exploiting such CASS knowledge to change the way in which devices and circuits are understood, optimized, and leveraged in a variety of systems and applications.


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Periodicals related to Image retrieval

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Aerospace and Electronic Systems Magazine, IEEE

The IEEE Aerospace and Electronic Systems Magazine publishes articles concerned with the various aspects of systems for space, air, ocean, or ground environments.


Audio, Speech, and Language Processing, IEEE Transactions on

Speech analysis, synthesis, coding speech recognition, speaker recognition, language modeling, speech production and perception, speech enhancement. In audio, transducers, room acoustics, active sound control, human audition, analysis/synthesis/coding of music, and consumer audio. (8) (IEEE Guide for Authors) The scope for the proposed transactions includes SPEECH PROCESSING - Transmission and storage of Speech signals; speech coding; speech enhancement and noise reduction; ...


Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


Circuits and Systems for Video Technology, IEEE Transactions on

Video A/D and D/A, display technology, image analysis and processing, video signal characterization and representation, video compression techniques and signal processing, multidimensional filters and transforms, analog video signal processing, neural networks for video applications, nonlinear video signal processing, video storage and retrieval, computer vision, packet video, high-speed real-time circuits, VLSI architecture and implementation for video technology, multiprocessor systems--hardware and software-- ...


Computer Graphics and Applications, IEEE

IEEE Computer Graphics and Applications (CG&A) bridges the theory and practice of computer graphics. From specific algorithms to full system implementations, CG&A offers a strong combination of peer-reviewed feature articles and refereed departments, including news and product announcements. Special Applications sidebars relate research stories to commercial development. Cover stories focus on creative applications of the technology by an artist or ...


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Most published Xplore authors for Image retrieval

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Xplore Articles related to Image retrieval

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Correction to "The Bayesian image retrieval system, pichunter: theory, implementation, and psychophysical experiments"

IEEE Transactions on Image Processing, 2000

None


Mapping by adaptive threshold method for dimension reduction of content-based indexing and retrieval features

2005 13th European Signal Processing Conference, 2005

Dimension reduction methods have been commonly used for content-based multimedia indexing and retrieval. In this paper, we investigate the use of a mapping by adaptive threshold (MAT) method for dimension reduction of feature data. The proposed MAT method is implemented and compared to two other well- known dimension reduction methods, namely Principal Component Analysis and Multidimensional Scaling. Experimental studies on ...


Content-based retrieval and real time detection from video sequences acquired by surveillance systems

Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269), 1998

A surveillance system devoted to detect abandoned objects in unattended environments is presented; image processing content based retrieval capabilities have been added to make the inspection task of operators easier. Video based surveillance systems generally employ one or more cameras connected to a set of monitors. This kind of system needs the presence of a human operator, who interprets the ...


Web Image Clustering Based on Multi-instance

2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2008

In image retrieval and annotation, multi-instance learning has been studied actively. Most of the methods solve the MIL problem in a supervised way. In this paper, we proposed two unsupervised frameworks for clustering multi- instance objects based on expectation maximization (EM) and iterative heuristic optimization respectively. For each framework, we introduced three new algorithms of finding users' interests on specific ...


YACBIR: Yet Another Content Based Image Retrieval System

2010 14th International Conference Information Visualisation, 2010

Vision is central in human perception. Images are everywhere. Real life applications produce and use huge amounts of different types images. Retrieving an image having some characteristics in a big database is a crucial task. We need then mechanisms for indexing and retrieving images. CBIR (Content Based Image Retrieval) systems perform these tasks by indexing images using the physical characteristics ...


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Educational Resources on Image retrieval

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IEEE-USA E-Books

  • Correction to "The Bayesian image retrieval system, pichunter: theory, implementation, and psychophysical experiments"

    None

  • Mapping by adaptive threshold method for dimension reduction of content-based indexing and retrieval features

    Dimension reduction methods have been commonly used for content-based multimedia indexing and retrieval. In this paper, we investigate the use of a mapping by adaptive threshold (MAT) method for dimension reduction of feature data. The proposed MAT method is implemented and compared to two other well- known dimension reduction methods, namely Principal Component Analysis and Multidimensional Scaling. Experimental studies on image retrieval reveal that the proposed method successfully reduces the dimension of feature vectors without degrading semantic image retrieval performance significantly. Furthermore, its computational complexity is significantly less than the other methods.

  • Content-based retrieval and real time detection from video sequences acquired by surveillance systems

    A surveillance system devoted to detect abandoned objects in unattended environments is presented; image processing content based retrieval capabilities have been added to make the inspection task of operators easier. Video based surveillance systems generally employ one or more cameras connected to a set of monitors. This kind of system needs the presence of a human operator, who interprets the acquired information and controls the evolution of the events in a surveyed environment. During the last years, efforts have been made to develop systems supporting human operators in their surveillance task, in order to focus the attention of operators when unusual situations are detected. Image sequence databases are also managed by the proposed surveillance system in order to provide operators with the possibility of retrieving in a second time the interesting sequences that may contain useful information for discovering causes of an alarm. Experimental results are shown in terms of the probability of correct detection of abandoned objects and examples concerning retrieval sequences.

  • Web Image Clustering Based on Multi-instance

    In image retrieval and annotation, multi-instance learning has been studied actively. Most of the methods solve the MIL problem in a supervised way. In this paper, we proposed two unsupervised frameworks for clustering multi- instance objects based on expectation maximization (EM) and iterative heuristic optimization respectively. For each framework, we introduced three new algorithms of finding users' interests on specific Web images without any manual labeled data. And comparative studies have shown the effectiveness of the proposed algorithms.

  • YACBIR: Yet Another Content Based Image Retrieval System

    Vision is central in human perception. Images are everywhere. Real life applications produce and use huge amounts of different types images. Retrieving an image having some characteristics in a big database is a crucial task. We need then mechanisms for indexing and retrieving images. CBIR (Content Based Image Retrieval) systems perform these tasks by indexing images using the physical characteristics automatically extracted and searching by an image query. We will present a CBIR system named YACBIR (Yet Another CBIR) that combines several properties (color, texture and points of interest) extracted automatically to index and retrieve images.

  • Secured biometric template

    User authentication and personal privacy is an important aspect of reliable information system. Biometric recognition with cryptography provides a reliable solution for user authentication and identity management. This paper proposes a unique way of creating biometric template using palm-dorsa vein pattern and enhancing its security level by encrypting with elliptic hashing, called biometric encrypted template. The advantage of using elliptic hashing is that, biometric template is easily encrypted and retrieval of original image from the encrypted template is impossible. Crypto analysis suggests that the proposed algorithm provides Confidentiality, authentication, integrity.

  • Image retrieval using NN based pre-classification and fuzzy relevance feedback

    In this article, we have proposed an interactive image retrieval scheme using MPEG-7 visual features, Neural Network (NN)-based pre-classifier and fuzzy based feature evaluation scheme. The performance of the existing image retrieval systems is generally limited due to semantic gap, resulted due to the discrepancies between the computed low-level features and user's conception of an image. Partitioning the database by a NN-based pre- classifier, and using a fuzzy based feature evaluation scheme, the performance of the proposed scheme has been found to improved drastically by reducing the retrieval time and increasing the accuracy.

  • Trademark retrieval using contour-skeleton stroke classification

    In this paper we propose a method to retrieve trademarks using query by sketches. The trademark images are first filtered to remove noise. Then each filtered image is segmented into regions based on pixel connectivity. For each region, a decision is made about whether thinning or edge extraction should be applied. Afterwards stroke tracing is performed to extract the sketch of the trademark. The user can then provide a query sketch that will be compared with those extracted sketches from the database trademark images in order to retrieve similar trademarks. Query by sketch is useful in the trademark retrieval application since the user can search for similar trademarks by providing a rough sketch instead of keywords. Our approach saves a lot of labor because it does not require the trademark database to be manually annotated with keywords and the experimental result shows that our scheme outperforms an existing method in terms of retrieval performance.

  • Effective object tracking by matching object and background models using active contours

    In this paper, we propose an effective approach for tracking distribution of objects. The approach uses a competition between a tracked objet and background distributions using active contours. Only the segmentation of the object in the first frame is required for initialization. We evolve the object contour by assigning pixels in a fashion that maximizes the likelihood of the object versus the background. This maximization is implemented using an EM- like algorithm, which evolves the object contour exactly to its boundaries, and adapts the parameters of the object and background distributions.

  • Contour-based feature extraction for image classification and retrieval

    We propose a feature extraction scheme for application on image classification and retrieval that is based on shapes' contours, while discarding information within the boundaries such as colour and texture. The center of mass and opposite distances are calculated for every contour pixel and used to measure distances between pairs of images that are invariant to common transformations. We apply the k-nearest neighbours (k-NN) algorithm to classify/retrieve a query image according to the k closest images' classes. The resulting success rates were computed for the Kimia, MPEG-7 and Tari image data sets and compared with those of other techniques.



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