X-ray image retrieval system based on visual feature discrimination

Alraqibah, Heelah A.; Bchir, Ouiem; Ismail, Mohamed Maher Ben

doi:10.1117/12.2064512

Cited by 6 publications

(7 citation statements)

References 9 publications

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“…In particular, image categorization/classification has been designed as a preprocessing phase to speed up image retrieval from the large collection [76,77]. Equivalently, unsupervised learning has been adapted to speedup retrieval process and enhances visualization performance when the images are not labelled or annotated [13,14]. More specifically, the clustering phase can be represented as early retrieval stage hat aims to handle unstructured image collections.…”

Section: A Supervised and Unsupervised Learningmentioning

confidence: 99%

“…Other surveys have been elaborated on highly relevant topics to CBIR systems. Namely, researches on high-dimensional data indexing [11], relevance feedback [10], and medical application of CBIR [13,14] have been surveyed. The continuous growth of associated research spanning several domains during the last decade and the increase in the number of researchers investigating CBIR are the main motivations of this survey.…”

Section: Introductionmentioning

confidence: 99%

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A Survey on Content-based Image Retrieval

Ismail¹

2017

ijacsa

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Abstract-The widespread of smart devices along with the exponential growth of virtual societies yield big digital image databases. These databases can be counter-productive if they are not coupled with efficient Content-Based Image Retrieval (CBIR) tools. The last decade has witnessed the introduction of promising CBIR systems and promoted applications in various fields. In this article, a survey on state of the art content based image retrieval including empirical and theoretical work is proposed. This work also includes publications that cover research aspects relevant to CBIR area. Namely, unsupervised and supervised learning and fusion techniques along with lowlevel image visual descriptors have been reported. Moreover, challenges and applications that emerged to support CBIR research have been discussed in this work.

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Section: A Supervised and Unsupervised Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Survey on Content-based Image Retrieval

Ismail¹

2017

ijacsa

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“…The query content is then used to mine the database. Several CBIR approaches have been reported in the literature [ 1 , 2 , 3 , 4 ] during the last decade, and other CBIR applications have been proposed recently [ 5 , 6 , 7 ]. For these CBIR systems, the visual properties of an image are described using low-level feature descriptors [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Multiple Query Content-Based Image Retrieval Using Relevance Feature Weight Learning

2020

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We propose a novel multiple query retrieval approach, named weight-learner, which relies on visual feature discrimination to estimate the distances between the query images and images in the database. For each query image, this discrimination consists of learning, in an unsupervised manner, the optimal relevance weight for each visual feature/descriptor. These feature relevance weights are designed to reduce the semantic gap between the extracted visual features and the user’s high-level semantics. We mathematically formulate the proposed solution through the minimization of some objective functions. This optimization aims to produce optimal feature relevance weights with respect to the user query. The proposed approach is assessed using an image collection from the Corel database.

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“…As customary hue capacities used in CBIR, there are shading histogram, shade correlogram, shading shape descriptor, and scale shade descriptor [3]. Shading histogram is the most extreme usually utilized shading portrayal plan to symbolize the worldwide element structure of a photograph, however it doesn't have any spatial information.…”

Section: Introductionmentioning

confidence: 99%

“…It is invariant to interpretation and revolution of a photograph and normalizing the histogram result in scale invariance Texture is utilized to indicate the unpleasantness or coarseness of the protest floor and depicted as an example with a couple of kind of consistency. Surface capacity has been utilized as a part of different bundles beginning from business application to medicinal imaging [3]. There are various calculations for surface assessment utilized by scientists, which incorporates Gray coevents grid, Markov arbitrary field, 'concurrent vehicle backward (SAR)' rendition, universal deterioration adaptation, Gabor sifting, wavelet decay et cetera.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of Retrieval of Images Based on Color, Text and Shape

Jeyalaksshmi¹,

Prasanna²

2017

International Journal of Innovative Research in Applied Sciences and Engineering

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ABSTRACT:Retrieving the image based on the content is one of the most significant research fields in the digital image processing area. The image retrieval is becoming an innovative and quickest way of retrieving the image. Many digital images and videos are uploaded and transitioned everyday, because of the social networking medium development. Google, Yahoo and many search engines are accessing the large collection of database based on the textual annotation. These images are annotated manually with keywords, descriptions and then the images are retrieved using text-based searching methods. Therefore, performances of these systems are not satisfactory. So, we need new techniques. These techniques will easy to handle the data and can easily access the data. The CBIR method is working on the basis of particular image feature such as color, texture and shape which are derived automatically. It uses digital processing and analysis to automatically generate descriptions directly from the media data. This paper represents how the implementation of image retrieval is done based on the content.

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X-ray image retrieval system based on visual feature discrimination

Cited by 6 publications

References 9 publications

A Survey on Content-based Image Retrieval

A Survey on Content-based Image Retrieval

Multiple Query Content-Based Image Retrieval Using Relevance Feature Weight Learning

Design and Implementation of Retrieval of Images Based on Color, Text and Shape

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