CBIR-DSN: integrating clustering and retrieval platforms for disk space narrowing degradation assessment

Mustapha, Aouache; Hussain, Aini; Ahmad, Wan Juliana Wan; Zaki, Wan Mimi Diyana Wan; Hamid, Hamzaini Bin Abdul

doi:10.1007/s11042-019-7176-5

Cited by 4 publications

(4 citation statements)

References 40 publications

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“…The EDRA is a tremendous get-together of dermatoscopic pictures that was appropriated together with the Interactive Atlas of Dermoscopy. 17 We isolated the dataset to cover just researched with more than 50 models and that was suffering with the ISIC2017 dataset. A full scale 20% of pictures, randomized and stratified by examination of cases, were autonomous as a test-set to evaluate our bearing.…”

Section: A Datasetmentioning

confidence: 99%

“…It limited the following condition to make what is known as a delicate edge hyper plane. (17) SVM testing: Initially, the request image is given as the say for retrieving the images from the data base which is applicable to the query image. Then the query image is preprocessed and the features are extracted.…”

Section: Svm For Non-straightly Distinguishable Informationmentioning

confidence: 99%

“…The pictures of Femur, Humerus, and Knee influenced by the sickness Avascular Necrosis are used to dissect the IR appear by paying the proposed IR-TBFE-SVM. For the judgment reason the [17] and [18] frameworks are connected and tried with the proposed framework.…”

Section: A Performance Analysismentioning

confidence: 99%

“…The performance analysis of the EDRA dataset is shown in the below figure 3. In that the proposed system is compared with the Existing [17] and [18] the proposed system provides much better results in terms of accuracy, precision and recall.…”

Section: Recallmentioning

confidence: 99%

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Hybrid Tile Based Feature Extraction and Support Vector Machine Base Content-Based Image Retrieval System for Medical Application

Malathy¹,

Rajinikanth²,

Mohan³

et al. 2019

IJRTE

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Through the landing of therapeutic endoscopes, earth perception satellites and individual telephones, content-based picture recovery (CBIR) has concerned critical consideration, activated by its broad applications, e.g., medicinal picture investigation, removed detecting, and individual re-distinguishing proof. Be that as it may, developing successful component extraction is as yet reported as an invigorating issue.In this paper, to overcome the feature extraction problems a hybrid Tile Based Feature Extraction (TBFE) is introduced. The TBFE algorithm is hybrid with the local binary pattern (LBP) and Local derivative pattern (LDP). These hybrid TBFE feature extraction method helps to extract the color image features in automatic manner. Support vector machine (SVM) is used as a classifier in this image retrieval approach to retrieve the images from the database. The hybrid TBFE along with the SVM classifier image retrieval is named as IR-TBFE-SVM. Experiments show that IR-TBFE-SVMdelivers a higher correctness and recall rate than single feature employed retrieval systems, and ownsdecentweight balancing and query efficiency performance.

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Section: A Datasetmentioning

confidence: 99%

Section: Svm For Non-straightly Distinguishable Informationmentioning

confidence: 99%

Section: A Performance Analysismentioning

confidence: 99%

Section: Recallmentioning

confidence: 99%

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