A Microcomputer-Based Image Database Management System

Prasad, Bandreddi E.; Gupta, Amar; Toong, Hoo-Min D.; Madnick, Stuart E.

doi:10.1109/tie.1987.350929

Cited by 20 publications

(5 citation statements)

References 14 publications

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“…The specific requirements for an end-user's ability to create, modify, query, or export a given item of research data depends on the site and role of the user requesting the data manipulation event (Prasad et al, 1987 ; Brinkley and Rosse, 2002 ; Bota and Arbib, 2004 ; Costa, 2004 ; Bota et al, 2005 ; Jovicich et al, 2005 ). We have developed tools for attaching roles to portal users, such as principal investigator, co investigator, study coordinator, rater, etc.…”

Section: Methodsmentioning

confidence: 99%

Mining the mind research network: a novel framework for exploring large scale, heterogeneous translational neuroscience research data sources

Bockholt

Scully

Courtney

et al. 2010

Front. Neuroinform.

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A neuroinformatics (NI) system is critical to brain imaging research in order to shorten the time between study conception and results. Such a NI system is required to scale well when large numbers of subjects are studied. Further, when multiple sites participate in research projects organizational issues become increasingly difficult. Optimized NI applications mitigate these problems. Additionally, NI software enables coordination across multiple studies, leveraging advantages potentially leading to exponential research discoveries. The web-based, Mind Research Network (MRN), database system has been designed and improved through our experience with 200 research studies and 250 researchers from seven different institutions. The MRN tools permit the collection, management, reporting and efficient use of large scale, heterogeneous data sources, e.g., multiple institutions, multiple principal investigators, multiple research programs and studies, and multimodal acquisitions. We have collected and analyzed data sets on thousands of research participants and have set up a framework to automatically analyze the data, thereby making efficient, practical data mining of this vast resource possible. This paper presents a comprehensive framework for capturing and analyzing heterogeneous neuroscience research data sources that has been fully optimized for end-users to perform novel data mining.

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Section: Methodsmentioning

confidence: 99%

Mining the mind research network: a novel framework for exploring large scale, heterogeneous translational neuroscience research data sources

Bockholt

Scully

Courtney

et al. 2010

Front. Neuroinform.

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“…al. [28] developed the first microcomputer-based image database retrieval system. After the successful launch of the Google Image Search Product in 2001, various image retrieval systems are deployed for public usage.…”

Section: Related Workmentioning

confidence: 99%

Demystifying Core Ranking in Pinterest Image Search

Zhu¹

2018

Preprint

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Pinterest Image Search Engine helps millions of users discover interesting content everyday. This motivates us to improve the image search quality by evolving our ranking techniques. In this work, we share how we practically design and deploy various ranking pipelines into Pinterest image search ecosystem. Specifically, we focus on introducing our novel research and study on three aspects: training data, user/image featurization and ranking models. Extensive offline and online studies compared the performance of different models and demonstrated the efficiency and effectiveness of our final launched ranking models.

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“…The first image database retrieval system was developed at MIT, in 1987 by Banireddy Prasad, Amar Gupta, Hoo-min Toong, and Stuart Madnick [1]. They have designed microcomputer based IRS.…”

Section: Introductionmentioning

confidence: 99%

Image Retrieval System Using Intuitive Descriptors

Joshi¹,

Bhavsar²,

Sanghvi³

2014

Procedia Technology

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With the increase in the popularity of using huge image databases in various image retrieval applications, a need arises to develop an efficient, robust and automatic system which provides output in the form of similar images with respect to the input or query image. In this paper, three Image Retrieval Systems using very basic but novel descriptors, which can retrieve black and white (binary) image, grayscale image and color image from the binary, grayscale and color image databases, respectively, were implemented. It should be noted that the features used for the binary image and grayscale image may not be efficiently applicable to the color images. Obtained results indicate the same and it additionally shows that irrespective of numbers of images in database, developed Image Retrieval Systems retrieve images in very short period of time with high accuracy. Accuracy in color image retrieval is less compared to binary and grayscale image retrieval. So, it creates a need to design highly effective features for the color image retrieval.

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A Microcomputer-Based Image Database Management System

Abstract: Industrial applications frequently involve manipulation and management of pictorial information.

Cited by 20 publications

References 14 publications

Mining the mind research network: a novel framework for exploring large scale, heterogeneous translational neuroscience research data sources

Mining the mind research network: a novel framework for exploring large scale, heterogeneous translational neuroscience research data sources

Demystifying Core Ranking in Pinterest Image Search

Image Retrieval System Using Intuitive Descriptors

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