Image retrieval based on colour and improved NMI texture features

Du, Anyu; Wang, Liejun; Qin, Jiwei

doi:10.1080/00051144.2019.1645977

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…In Mistry et al (2016), the feature descriptor is generated using a fusion of DWT in YCbCr color space and LBP. In Du et al (2019), a weighted fusion of HSV color space-based histogram, GLCM, LBP, and normalized moment of inertia (NMI) with particle swarm optimization-based pulse code neural network (PCNN) is proposed. Yu et al (2013) present the fusion of SIFT and LBP features with the Kmeans clustering algorithm.…”

Section: Related Workmentioning

confidence: 99%

Image Retrieval Using Fusion of Sauvola and Thepade’s Sorted Block Truncation Coding-Based Color Features

Dewan¹,

Thepade²

2023

JST

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Because of the tremendous growth in digital imaging, enhanced communication and storage technology, billions of images are captured, stored, and exchanged daily. Finding and searching for an image in a large collection is becoming challenging. The query by reference image retrieval (IR) technique aims to close the semantic gap between the query and retrieve images while improving performance. The primary goal of the work proposed here is to develop discriminative and descriptive features of the image with the minimum possible size. Here, the weighted feature fusion-based IR technique is proposed using Sauvola local thresholding (SLT) and Thepade’s Sorted Block Truncation Coding (SBTC) methods. The proposed technique is tested using two standard datasets with mean square error (MSE) as a distance measure and average retrieval accuracy (ARA) as a performance metric. The technique has contributed to the enhancement of ARA with the small and fixed-size image feature vector. The feature vector generated is much smaller than the image dimension and is used as a feature vector to represent the image for retrieval. Results prove that the proposed technique of SBTC 8-ary with 0.1 weight and SLT with 0.9 weight feature fusion gives better ARA than other techniques studied.

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Section: Related Workmentioning

confidence: 99%

Image Retrieval Using Fusion of Sauvola and Thepade’s Sorted Block Truncation Coding-Based Color Features

Dewan¹,

Thepade²

2023

JST

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“…In [80], authors have presented a technique to fuse the texture and color features. e color features are generated using a histogram of the quantized HSV color space image.…”

Section: Feature Fusion-based Techniques Used Inmentioning

confidence: 99%

Image Retrieval Using Low Level and Local Features Contents: A Comprehensive Review

Dewan¹,

Thepade²

2020

Applied Computational Intelligence and Soft Computing

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Billions of multimedia data files are getting created and shared on the web, mainly social media websites. The explosive increase in multimedia data, especially images and videos, has created an issue of searching and retrieving the relevant data from the archive collection. In the last few decades, the complexity of the image data has increased exponentially. Text-based image retrieval techniques do not meet the needs of the users due to the difference between image contents and text annotations associated with an image. Various methods have been proposed in recent years to tackle the problem of the semantic gap and retrieve images similar to the query specified by the user. Image retrieval based on image contents has attracted many researchers as it uses the visual content of the image such as color, texture, and shape feature. The low-level image features represent the image contents as feature vectors. The query image feature vector is compared with the dataset images feature vectors to retrieve similar images. The main aim of this article is to appraise the various image retrieval methods based on feature extraction, description, and matching content that has been presented in the last 10–15 years based on low-level feature contents and local features and proposes a promising future research direction for researchers.

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A Framework of a Filtering and Classification Techniques for Enhancing the Accuracy of a Hybrid CBIR System

Narwal

Bhardwaj

Dhingra

2022

Mobile Radio Communications and 5G Networks

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Image retrieval based on colour and improved NMI texture features

Cited by 5 publications

References 18 publications

Image Retrieval Using Fusion of Sauvola and Thepade’s Sorted Block Truncation Coding-Based Color Features

Image Retrieval Using Fusion of Sauvola and Thepade’s Sorted Block Truncation Coding-Based Color Features

Image Retrieval Using Low Level and Local Features Contents: A Comprehensive Review

A Framework of a Filtering and Classification Techniques for Enhancing the Accuracy of a Hybrid CBIR System

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