RETRACTED ARTICLE: Essentiality for bridging the gap between low and semantic level features in image retrieval systems: an overview

Nair, Lakshmi; Subramaniam, Kamalraj; PrasannaVenkatesan, G. K. D.; Baskar, P. S.; Jayasankar, T.

doi:10.1007/s12652-020-02139-z

Cited by 18 publications

(7 citation statements)

References 52 publications

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“…Recently, it was concluded that this problem can be solved and the gap minimized based on two solutions, which take into consideration part of the domain-specific knowledge and apply some machine learning methods to produce a more advanced and intelligent model. This could be trained and used as an alternative method to extract features for calculating similarity (Nair et al, 2021). The traditional CBIR system (also known as instancebased CBIR system) usually has better retrieval outcomes and good performance results when a database contains relatively fewer images.…”

Section: Cbir Sbir and Deep Learning For Medical Image Retrievalmentioning

confidence: 99%

Pre-trained convolution neural networks models for content-based medical image retrieval

Ahmed¹,

Almagrabi²,

Osman³

2022

Int. j. adv. appl. sci.

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Content-based image retrieval (CBIR) is a recent method used to retrieve different types of images from repositories. The traditional content-based medical image retrieval (CBMIR) methods commonly used low-level image representation features extracted from color, texture, and shape image descriptors. Since most of these CBMIR systems depend mainly on the extracted features, the methods used in the feature extraction phase are more important. Features extraction methods, which generate inaccurate features, lead to very poor performance retrieval because of semantic gap widening. Hence, there is high demand for independent domain knowledge features extraction methods, which have automatic learning capabilities from input images. Pre-trained deep convolution neural networks (CNNs), the recent generation of deep learning neural networks, could be used to extract expressive and accurate features. The main advantage of these pre-trained CNNs models is the pre-training process for huge image data of thousands of different classes, and their knowledge after the training process could easily be transferred. There are many successful models of pre-trained CNNs models used in the area of medical image retrieval, image classification, and object recognition. This study utilizes two of the most known pre-trained CNNs models; ResNet18 and SqueezeNet for the offline feature extraction stage. Additionally, the highly accurate features extracted from medical images are used for the CBMIR method of medical image retrieval. This study uses two popular medical image datasets; Kvasir and PH2 to show that the proposed methods have good retrieval results. The retrieval performance evaluation measures of our proposed method have average precision of 97.75% and 83.33% for Kvasir and PH2 medical images respectively, and outperform some of the state-of-the-art methods in this field of study because these pre-trained CNNs have well trained layers among a huge number of image types. Finally, intensive statistical analysis shows that the proposed ResNet18-based retrieval method has the best performance for enhancing both recall and precision measures for both medical images.

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Section: Cbir Sbir and Deep Learning For Medical Image Retrievalmentioning

confidence: 99%

Pre-trained convolution neural networks models for content-based medical image retrieval

Ahmed¹,

Almagrabi²,

Osman³

2022

Int. j. adv. appl. sci.

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“…Fortunately, video analytics technology allows video platforms to easily detect the content of videos. The computer vision techniques adopted for this problem include object detection (Zou et al, 2019), object parsing (Salari et al, 2022), and image retrieval (Nair et al, 2021). In addition, the number of studies on online video advertising has grown exponentially in recent years.…”

Section: Introductionmentioning

confidence: 99%

A survey of online video advertising

Zhang

Yan

et al. 2023

WIREs Data Min & Knowl

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With the development of social media and the ubiquity of the Internet, recent years have witnessed the rapid development of online video advertising among publishers and advertisers. Video advertising, as a new type of advertisement, has gained significant research attention from both academia and industry, coinciding with the ever-growing volume of online videos. In this research, we provide a comprehensive survey of online video advertising in the fields of social science and computer science. We investigate state-of-the-art articles from 1990 to the present and provide a new taxonomy of extant research topics based on these articles. We also highlight the factors that cause advertising to affect people and the most popular video advertising techniques used in computer science. Finally, on the basis of the analytics of the surveyed papers, future challenges are identified and potential solutions to these are discussed.

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“…They use terminology that radiologists use to express their image observations, hence they are suitable for CBIR applications. With regards to visual descriptions of images, adding semantics can thus be an innovative way to bridge the semantic gap between visuals and their meaning [6]. Medical image search results have been found to improve using a combination of text attribute search, which relies on the content of the image, and low-level visual characteristics, which are derived directly from the content of the image [7][8].…”

Section: Introductionmentioning

confidence: 99%

Mammogram Image Retrieval System Using Texture and Semantic Features

Sharma

2022

J. Phys.: Conf. Ser.

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Image retrieval systems might help radiologists in aiding their diagnostic decision-making by giving a way to discover and identify similar images from databases. Keyword-based search became the dominant paradigm for searching multimedia datasets in the early years of image retrieval. However, using keywords alone has numerous drawbacks: human annotation is time-consuming and intrinsically incomplete, and the relationship between words and concepts is sometimes complicated. These significant challenges have motivated research in the field of Content-Based Image Retrieval (CBIR). However, using visual content has its own set of restrictions, owing to the Semantic Gap, which describes the disparity between low-level information that can be extracted quickly from images and high-level descriptions that are meaningful to users. As a result, a system with integrated approaches is important. In this paper a mammogram image retrieval system based on low level visual features and high-level semantic features has been proposed. Based upon the proposed framework, a prototype of mammogram image retrieval system has been developed. The proposed framework for retrieval of mammograms consists of two phases. The system first exploits the textual features and then further refines the search using visual features. Further, if the user is unsatisfied with the search results, he or she can send Relevance Feedback (RF) to the retrieval system, which has methods to learn about the user’s information needs. A system like this might be utilised for Computer Aided Diagnosis, medical education, and research. The framework established is generalizable and adaptable to a variety of anatomic and diagnostic circumstances. The proposed system’s performance is demonstrated by the experimental findings.

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RETRACTED ARTICLE: Essentiality for bridging the gap between low and semantic level features in image retrieval systems: an overview

Cited by 18 publications

References 52 publications

Pre-trained convolution neural networks models for content-based medical image retrieval

Pre-trained convolution neural networks models for content-based medical image retrieval

A survey of online video advertising

Mammogram Image Retrieval System Using Texture and Semantic Features

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