Comparison of different CBIR techniques

Madugunki, Meenakshi; Bormane, D. S.; Bhadoria, Sonali; Dethe, C. G.

doi:10.1109/icectech.2011.5941923

Cited by 25 publications

(5 citation statements)

References 17 publications

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“…In other side, Philippe and Matthieu [50] introduced in their paper the most known active learning methods for image retrieval such as Bayes classification, k-Nearest Neighbors [51], neural networks [52,53], wavelet network [54,55], lattice trees [56][57][58], Gaussian mixtures and support vector machines. Ekta and Hardeep [59] proposed the use of bayesian algorithm, as a supervised learning and a statistical method for classification, by reducing the noise from images.…”

Section: Low-level Content Approachesmentioning

confidence: 99%

A hybrid approach for Content-Based Image Retrieval based on Fast Beta Wavelet network and fuzzy decision support system

ElAdel

Ejbali

Zaied

et al. 2016

Machine Vision and Applications

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Content-based image retrieval (CBIR) has been a worthwhile topic for research for several years. A powerful CBIR system should minimize the semantic gap between the low-level features and high-level concepts in order to satisfy users requirements. Moreover, it should take into consideration the execution time. In this paper, we present a new semantic approach for CBIR supported by a parallel aggregation of content-based features extraction (shape, texture, color) using fuzzy support decision mechanisms. Shape features are based on Fast Beta Wavelet Network modeling and Hue moments. The texture descriptor is based on Energy computing at different decomposition levels. Finally, we present an implementation of a new color feature extraction based on fuzzy indexed color map. In the second stage, we propose a Fuzzy Decision Support System for feature (shape, texture, color) aggregation to improve the retrieval performance. The proposed approach is tested on four most popular datasets: Wang, INRIA Holidays, UKBench and samples from ImageNet, and the experiments showed that B Asma ElAdel the proposed approach can achieve a satisfactory retrieval performance with an acceptable search time.

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Section: Low-level Content Approachesmentioning

confidence: 99%

A hybrid approach for Content-Based Image Retrieval based on Fast Beta Wavelet network and fuzzy decision support system

ElAdel

Ejbali

Zaied

et al. 2016

Machine Vision and Applications

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“…It is a process in which an image is given to a system and searching is performed on the complete dataset of images to find the similar images based oncolor, size, texture. Histogram Equalization, Standard Deviation,Discrete wavelet transform, HSL (Hue, Saturation, Lightness) and HSV (Hue, Saturation, Value) are some of the parameters that can be used for comparison of various feature matching techniques [1].…”

Section: Content-based Image Retrievalmentioning

confidence: 99%

Classification of Biomedical Images Using Content Based Image Retrieval Systems

Zhang

Cui

et al. 2020

Int. J. Eng. Tech. Mgmt. Res.

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Because of the numerous application of Content-based image retrieval (CBIR) system in various areas, it has always remained a topic of keen interest by the researchers. Fetching of the most similar image from the complete repository by comparing it to the input image in the minimum span of time is the main task of the CBIR. The purpose of the CBIR can vary from different types of requirements like a diagnosis of the illness by the physician, crime investigation, product recommendation by the e-commerce companies, etc. In the present work, CBIR is used for finding the similar patients having Breast cancer. Gray-Level Co-Occurrence Matrix along with histogram and correlation coefficient is used for creating CBIR system. Comparing the images of the area of interest of a present patient with the complete series of the image of a past patient can help in early diagnosis of the disease. CBIR is so much effective that even when the symptoms are not shown by the body the disease can be diagnosed from the sample images.

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“…Catatan oleh setiap pakar radiologi adalah berbeza dan tugas ini amat memerihkan jika melibatkan pangkalan data imej yang besar. Oleh itu, kelemahan-kelemahan dalam TBIRS tersebut telah mendorong ahli-ahli kajian memberikan tumpuan kepada CBIRS (Arakeri et al 2012;Madugunki et al 2011).…”

Section: Tbirs Dan Cbirsunclassified

“…Kini, kajian dalam CBIRS lebih popular berbanding dengan TBIRS (Guo et al 2016;Arakeri and Ram Mohana Reddy 2013;Madugunki et al 2011). Menurut (Kumar et al 2013), staf klinikal memilih kes yang serupa dengan mengutamakan ciri-ciri visual dalam menjalankan diagnosis dan rawatan.…”

Section: Tbirs Dan Cbirsunclassified

Content-based Image Retrieval for Medical Application

Zaki¹,

Ahmad²,

Hussain³

et al. 2018

JKUKM

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ABSTRAK Dapatan semula imej (DSI) adalah sistem pencarian imej yang menggunakan ciri-ciri tertentu atau konteks khusus Kata kunci: Imej perubatan; sistem dapatan semula imej; aplikasi perubatan; modaliti x-ray; TBIRS ABSTRACT Image retrieval is an image searching system that uses certain characteristics or context in an image. In a medical field, image retrieval system has been used to provide the needed correct images to the physicians while the diagnosis and treatment process is being conducted. The earlier image retrieval system was a text-based image retrieval system (TBIRS) which using keyword for the image context and it requires human's help to manually make text annotation on the images. This system is still being widely used in the hospitals nowadays. The text annotation process is a laborious task especially when dealing with a huge database and is prone to human errors. To overcome the aforementioned issues, the approach of a content-based image retrieval system (CBIRS) with automatic indexing using visual features such as color

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Comparison of different CBIR techniques

Cited by 25 publications

References 17 publications

A hybrid approach for Content-Based Image Retrieval based on Fast Beta Wavelet network and fuzzy decision support system

A hybrid approach for Content-Based Image Retrieval based on Fast Beta Wavelet network and fuzzy decision support system

Classification of Biomedical Images Using Content Based Image Retrieval Systems

Content-based Image Retrieval for Medical Application

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