Greedy Learning of Deep Boltzmann Machine (GDBM)’s Variance and Search Algorithm for Efficient Image Retrieval

Ghrabat, Mudhafar Jalil Jassim; Ma, Guangzhi; Abduljabbar, Zaid Ameen; Sibahee, Mustafa A. Al; Jassim, Safa Jalil

doi:10.1109/access.2019.2948266

Cited by 16 publications

(6 citation statements)

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“…For GPU analysis, the input images are tested on an i3 processor with 4 GB RAM and NVIDIA GeForce GTX 1650. The proposed method is validated and tested outcomes are compared with the existing techniques such as IRFSM [28], Sketch‐based Image Retrieval by Salient Contour (SBIR) [29], Image Retrieval Scheme using Quantized Bins of Colour Image Components and Adaptive Tetrolet Transform (IR‐QBCI) [30] and Greedy Learning of Deep Boltzmann Machine (GDBM)'s Variance and Search Algorithm for Efficient Image Retrieval (GDBM‐IR) [31], considering metrics like accuracy, precision, recall, F‐measure, and retrieval time. Some of the sample images in the dataset are displayed below figure (Figure 5).…”

Section: Resultsmentioning

confidence: 99%

Deep image retrieval using artificial neural network interpolation and indexing based on similarity measurement

Ahmad

2022

CAAI Trans on Intel Tech

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In content-based image retrieval (CBIR), primitive image signatures are critical because they represent the visual characteristics. Image signatures, which are algorithmically descriptive and accurately recognized visual components, are used to appropriately index and retrieve comparable results. To differentiate an image in the category of qualifying contender, feature vectors must have image information's like colour, objects, shape, spatial viewpoints. Previous methods such as sketch-based image retrieval by salient contour (SBIR) and greedy learning of deep Boltzmann machine (GDBM) used spatial information to distinguish between image categories. This requires interest points and also feature analysis emerged image detection problems. Thus, a proposed model to overcome this issue and predict the repeating pattern as well as series of pixels that conclude similarity has been necessary. In this study, a technique called CBIR-similarity measure via artificial neural network interpolation (CBIR-SMANN) has been presented. By collecting datasets, the images are resized then subject to Gaussian filtering in the pre-processing stage, then by permitting them to the Hessian detector, the interesting points are gathered. Based on Skewness, mean, kurtosis and standard deviation features were extracted then given to ANN for interpolation. Interpolated results are stored in a database for retrieval. In the testing stage, the query image was inputted that is subjected to pre-processing, and feature extraction was then fed to the similarity measurement function. Thus, ANN helps to get similar images from the database. CBIR-SMANN have been implemented in the python tool and then evaluated for its performance. Results show that CBIR-SMANN exhibited a high recall value of 78% with a minimum retrieval time of 980 ms. This showed the supremacy of the proposed model was comparatively greater than the previous ones.

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Section: Resultsmentioning

confidence: 99%

Deep image retrieval using artificial neural network interpolation and indexing based on similarity measurement

Ahmad

2022

CAAI Trans on Intel Tech

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“…Ghrabat et al (2019) proposed the Multiple Ant Colony Optimization (MACO) method to locate pertinent characteristics, and it is used with all of the features [ 19 ]. The relevant factors are used for the Greedy learning of the Deep Boltzmann Machine classifier (GD-BM).…”

Section: Related Workmentioning

confidence: 99%

Remote sensing image information extraction based on Compensated Fuzzy Neural Network and big data analytics

Sun,

Zhang,

Liu

et al. 2024

BMC Med Imaging

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Medical imaging AI systems and big data analytics have attracted much attention from researchers of industry and academia. The application of medical imaging AI systems and big data analytics play an important role in the technology of content based remote sensing (CBRS) development. Environmental data, information, and analysis have been produced promptly using remote sensing (RS). The method for creating a useful digital map from an image data set is called image information extraction. Image information extraction depends on target recognition (shape and color). For low-level image attributes like texture, Classifier-based Retrieval(CR) techniques are ineffective since they categorize the input images and only return images from the determined classes of RS. The issues mentioned earlier cannot be handled by the existing expertise based on a keyword/metadata remote sensing data service model. To get over these restrictions, Fuzzy Class Membership-based Image Extraction (FCMIE), a technology developed for Content-Based Remote Sensing (CBRS), is suggested. The compensation fuzzy neural network (CFNN) is used to calculate the category label and fuzzy category membership of the query image. Use a basic and balanced weighted distance metric. Feature information extraction (FIE) enhances remote sensing image processing and autonomous information retrieval of visual content based on time-frequency meaning, such as color, texture and shape attributes of images. Hierarchical nested structure and cyclic similarity measure produce faster queries when searching. The experiment’s findings indicate that applying the proposed model can have favorable outcomes for assessment measures, including Ratio of Coverage, average means precision, recall, and efficiency retrieval that are attained more effectively than the existing CR model. In the areas of feature tracking, climate forecasting, background noise reduction, and simulating nonlinear functional behaviors, CFNN has a wide range of RS applications. The proposed method CFNN-FCMIE achieves a minimum range of 4–5% for all three feature vectors, sample mean and comparison precision-recall ratio, which gives better results than the existing classifier-based retrieval model. This work provides an important reference for medical imaging artificial intelligence system and big data analysis.

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“…A modified GA has been utilized for optimizing the features, and these features have been classified utilizing a novel SVM-based CNN (NSVMBCNN). In [34], a preprocessed method has been established, a method that utilizes median filtering (MF) for removing noise to attain enhanced accuracy and reliability. Afterward, Fourier and circularity descriptors can be extracted in the effectual method corresponding to the texture and affine shape adaptation features.…”

Section: Related Workmentioning

confidence: 99%

Archimedes Optimization Algorithm With Deep Learning Assisted Content-Based Image Retrieval in Healthcare Sector

Issaoui,

Alohali,

Mtouaa

et al. 2024

IEEE Access

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Content-based image retrieval (CBIR) in the healthcare field is an advanced technology that leverages the visual or content features of medical images to retrieve similar images from vast data. This technology has significant applications in healthcare, including medical diagnosis, research, and treatment planning. It is a diagnostic tool that enhances the explainability of computer-aided diagnoses (CAD) systems and provides decision-making support for healthcare professionals. A conventional way to CBIR is learning a distance metric by converting images into feature space where the distance between samples is a similarity measurement. CBIR systems can autonomously extract intricate features from medical images by leveraging convolutional neural networks (CNN) and other advanced deep learning techniques, enabling accurate and swift retrieval of relevant patient cases and diagnostic references. This manuscript designs an Archimedes Optimization Algorithm with Deep Learning Assisted Content-Based Image Retrieval in Healthcare Sector (AOADL-CBIRH) technique. The AOADL-CBIRH technique intends to retrieve similar images based on query images in the healthcare sector. In the presented AOADL-CBIRH technique, image pre-processing is initially performed using an adaptive bilateral filtering (ABF) approach to enhance the image quality. For deep feature extraction, the AOADL-CBIRH method applies the Efficient Channel Spatial Model (ECSM) with the ResNet50 model. To improve the retrieval performance, the AOADL-CBIRH technique employs AOA-based hyperparameter tuning for the ECSM-ResNet50 model. Lastly, the Manhattan distance metric determines the similarity between the images and retrieves them. The experimental evaluation of the AOADL-CBIRH algorithm is tested by using benchmark image dataset. The stimulation values signified the enhanced retrieval results of the AOADL-CBIRH technique over other models. INDEX TERMSContent-based image retrieval; Healthcare; Similarity measurement; Deep learning; Archimedes optimization algorithm I. INTRODUCTIONRecently, the accessibility and application of numerous medical imaging approaches namely positron emission tomography (PET), magnetic resonance imaging (MRI), and X-ray scans have led to a dramatic expansion in the size of medical image sources [1]. It highlights the requirement for emerging beneficial healthcare information management systems (HIMS), which are assisted in easy-to-use and instinctive applications like query-based image retrieval, similar image retrieval, and image classification to allow This article has been accepted for publication in IEEE Access.

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Greedy Learning of Deep Boltzmann Machine (GDBM)’s Variance and Search Algorithm for Efficient Image Retrieval

Cited by 16 publications

References 50 publications

Deep image retrieval using artificial neural network interpolation and indexing based on similarity measurement

Deep image retrieval using artificial neural network interpolation and indexing based on similarity measurement

Remote sensing image information extraction based on Compensated Fuzzy Neural Network and big data analytics

Archimedes Optimization Algorithm With Deep Learning Assisted Content-Based Image Retrieval in Healthcare Sector

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