State-of-Art: Similarity Assessment for Content Based Image Retrieval System

Patel, Bhagwandas; Yadav, Kuldeep; Ghosh, Debashis

doi:10.1109/isssc50941.2020.9358899

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…This model extracts image features automatically for testing as well as training images. The similarity measure is particularly significant in this procedure since it will be used to get similar images from the repository (13) .…”

Section: Methodsmentioning

confidence: 99%

An Investigation of Distance Measures for Development of Effective Content Based Tumor Image Retrieval System

Anitha,

Kalaiselvi

2024

IJST

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Background/Objectives: The MRI has proven to be extremely effective in detecting tumors, with millions of images created each day throughout the world. To find similar images from a vast collection, Content-Based Tumor Image Retrieval (CBTIR) technology has been used to analysis the medical image. In the traditional retrieval methods, retrieving a similar image from the large database is crucial task. To overcome this issue we developed deep learning based retrieval method. Methods: This research offers a retrieval approach based on predefined ResNet models for quick and accurate image retrieval. We tested various prominent ResNet models with different distance similarity metrics, and the best option was determined by this work. Findings: After the various evaluation of ResNet models with varied distance measures on the CE-MRI data set, ResNet50 model applied with Hamming distance yields 99.33% of retrieval precision. Novelty: This work used predefined ResNet models with the combination of Distance similarity metrics to achieve more accurate results on medical image retrieval compared to the other conventional methods. Keywords: Content Based Image Retrieval, Tumor Retrieval, Hamming Distance, Euclidean Distance, Minkowski

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Section: Methodsmentioning

confidence: 99%

An Investigation of Distance Measures for Development of Effective Content Based Tumor Image Retrieval System

Anitha,

Kalaiselvi

2024

IJST

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“…Seetharaman and Sathiamoorthy [24] applied the Manhattan similarity metric along with colour-based and shape-based features to complete a medical ImR task. Their method achieved an average retrieval rate of 84.47% and a speed of 2.29 s. Petal et al [23] extracted both colour-based and texture-based features from images and applied them to a CBIR task using various distance measures (e.g., Euclidean, cosine, Jaccard, Manhattan, etc.). Their approach achieved an accuracy of 87.2% in retrieving similar images.…”

Section: Related Work 21 Feature Extraction and Relevant Similarity M...mentioning

confidence: 99%

“…Feature extraction methods can extract the hidden features of irregular patterns within images and improve retrieval performance. These methods extract Local Binary Pattern (LBP) features [16][17][18][19], Scale-Invariant Feature Transform (SIFT) features [20][21][22], as well as colour and shape features [23,24] to conduct retrieval of images with irregular patterns. Distance-based similarity metrics (e.g., Manhattan, Jaccard, Euclidean, cosine, etc.)…”

Section: Introductionmentioning

confidence: 99%

Efficient Retrieval of Images with Irregular Patterns Using Morphological Image Analysis: Applications to Industrial and Healthcare Datasets

Zhang,

Cosma,

Bugby

et al. 2023

J. Imaging

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Image retrieval is the process of searching and retrieving images from a datastore based on their visual content and features. Recently, much attention has been directed towards the retrieval of irregular patterns within industrial or healthcare images by extracting features from the images, such as deep features, colour-based features, shape-based features, and local features. This has applications across a spectrum of industries, including fault inspection, disease diagnosis, and maintenance prediction. This paper proposes an image retrieval framework to search for images containing similar irregular patterns by extracting a set of morphological features (DefChars) from images. The datasets employed in this paper contain wind turbine blade images with defects, chest computerised tomography scans with COVID-19 infections, heatsink images with defects, and lake ice images. The proposed framework was evaluated with different feature extraction methods (DefChars, resized raw image, local binary pattern, and scale-invariant feature transforms) and distance metrics to determine the most efficient parameters in terms of retrieval performance across datasets. The retrieval results show that the proposed framework using the DefChars and the Manhattan distance metric achieves a mean average precision of 80% and a low standard deviation of ±0.09 across classes of irregular patterns, outperforming alternative feature–metric combinations across all datasets. Our proposed ImR framework performed better (by 8.71%) than Super Global, a state-of-the-art deep-learning-based image retrieval approach across all datasets.

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Novel Content Based Image Retrieval—Features of Correlated Visual Textons and MQLPP Descriptor

Sweeta,

Sivagami

2024

SN COMPUT. SCI.

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State-of-Art: Similarity Assessment for Content Based Image Retrieval System

Cited by 4 publications

References 19 publications

An Investigation of Distance Measures for Development of Effective Content Based Tumor Image Retrieval System

An Investigation of Distance Measures for Development of Effective Content Based Tumor Image Retrieval System

Efficient Retrieval of Images with Irregular Patterns Using Morphological Image Analysis: Applications to Industrial and Healthcare Datasets

Novel Content Based Image Retrieval—Features of Correlated Visual Textons and MQLPP Descriptor

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