An Efficient Content Based Image Retrieval using an Optimized Neural Network for Medical Application

Kumar, Ravin; Marikkannu, P.

doi:10.1007/s11042-020-08953-z

Cited by 12 publications

(4 citation statements)

References 15 publications

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“…Utilizing the content-based image retrieval technique, the collection images were tagged with additional content and information. Paper (12) examined dictionary learning (DL) tasks that involve CBIR and are suitable for using sparse vectors. The CNN architecture yields a vector for each image, which was utilized as the DL input.…”

Section: Contribution Of the Studymentioning

confidence: 99%

Unveiling Visual Treasures: Harnessing Deep Learning for Content-Based Image Retrieval

Anish,

Thiyagarajan

2024

IJST

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Objective: An essential aspect of computer vision is content-based image retrieval (CBIR), which enables users to search for images based on their visual content instead of created annotations. Advances in technology have resulted in a significant rise in the complexity of multimedia content and the emergence of new research fields centered on similar multimedia material retrieval. The efficacy of retrieval is impacted by the limits of the present CBIR systems, which result from overlooked algorithms and computing restrictions. Methods: This research introduces a novel approach employing the Siamese Edge Attention Layered Convonet (SEAL Convonet) for Image Retrieval. We utilize the CBIR image dataset through Gaussian smoothing to enhance image quality for data preprocessing and the Canny Edge Detector (CED) for edge detection, following pre-processing. The Histogram of Oriented Gradients (HOG) is used for feature extraction to extract complex textures and patterns from the images. Findings: This approach is implemented and tested through simulations as well as the results indicate a substantial positive deviation in the performance and retrieval of the images compared to existing methods. The performance metrics are accuracy (97 %), precision (94 %), recall (91 %), F1-Score (97 %), False Positive Rate (FNR) (0.0013), Matthew's correlation coefficient (MCC) (0.85), and False Negative Rate (FPR) (0.0036) show the measurements of this proposed model. Application: The state of the art in this work is researching the influence of optimizers on the accuracy process, as indicated by the findings. Keywords: CBIR, Gaussian smoothing, Canny Edge Detector, Histogram of oriented gradients (HOG), Siamese Edge Attention Layered convonet (SEAL Convonet), Database

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Section: Contribution Of the Studymentioning

confidence: 99%

Unveiling Visual Treasures: Harnessing Deep Learning for Content-Based Image Retrieval

Anish,

Thiyagarajan

2024

IJST

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“…Test this method using the COREL-10K dataset. [29] proposed from the image database, features of images including textures, shapes, standard deviations, and means are extracted. The K-means approach for clustering extracted features is provided.…”

Section: Deep Learningmentioning

confidence: 99%

An Overview of Content-Based Image Retrieval Methods and Techniques

Hadid,

Hussein,

Al-Qaysi

et al. 2023

ijcsm

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With the development of Internet technology and the popularity of digital devices, Content-Based Image Retrieval (CBIR) has been quickly developed and applied in various fields related to computer vision and artificial intelligence. Currently, it is possible to retrieve related images effectively and efficiently from a large-scale database with an input image. In the past ten years, great efforts have been made for new theories and models of CBIR, and many effective CBIR algorithms have been established. Content-based image retrieval helps to discover identical images in a big dataset that match a query image. The query image's representative feature similarities to the dataset images typically assist in ranking the images for retrieval. There are various past studies on different handicraft feature descriptors according to the visual features that describe the images: color, texture, and shape. However, deep learning has been the dominant alternative to manually planned feature engineering; it automatically takes the features from the data. The current work reviews recent advancements in content-based image retrieval. For a deeper understanding of the advancement, the explanation of current state-of-the-art approaches from various vantage points is also conducted. This review employs a taxonomy encompassing various retrieval networks, classification types, and descriptors and this study will help researchers make more progress in image retrieval

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“…PSO was used by [28] to improve the performance of relevance feedback in content based image retrieval. ANN weight optimization is performed by PSO to improve the performance of content based image retrieval [29]. Ranking of image retrieval was also improved by though PSO in [30].…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Hybrid Evolutionary Algorithm Based Relevance Feedback Approach for Image Retrieval

Mahmood¹,

Imran²,

Irtaza³

et al. 2022

Computers, Materials &Amp; Continua

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Searching images from the large image databases is one of the potential research areas of multimedia research. The most challenging task for nay CBIR system is to capture the high level semantic of user. The researchers of multimedia domain are trying to fix this issue with the help of Relevance Feedback (RF). However existing RF based approaches needs a number of iteration to fulfill user's requirements. This paper proposed a novel methodology to achieve better results in early iteration to reduce the user interaction with the system. In previous research work it is reported that SVM based RF approach generating better results for CBIR. Therefore, this paper focused on SVM based RF approach. To enhance the performance of SVM based RF approach this research work applied Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) before applying SVM on user feedback. The main objective of using these meta-heuristic was to increase the positive image sample size from SVM. Firstly steps PSO is applied by incorporating the user feedback and secondly GA is applied on the result generated through PSO, finally SVM is applied using the positive sample generated through GA. The proposed technique is named as Particle Swarm Optimization Genetic Algorithm-Support Vector Machine Relevance Feedback (PSO-G A-SVM-RF). Precisions, recall and F-score are used as performance metrics for the assessment and validation of PSO-GA-SVM-RF approach and experiments are conducted on coral image dataset having 10908 images. From experimental results it is proved that PSO-GA-SVM-RF approach outperformed then various well known CBIR approaches.

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An Efficient Content Based Image Retrieval using an Optimized Neural Network for Medical Application

Cited by 12 publications

References 15 publications

Unveiling Visual Treasures: Harnessing Deep Learning for Content-Based Image Retrieval

Unveiling Visual Treasures: Harnessing Deep Learning for Content-Based Image Retrieval

An Overview of Content-Based Image Retrieval Methods and Techniques

Hybrid Evolutionary Algorithm Based Relevance Feedback Approach for Image Retrieval

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