An extension of local mesh peak valley edge based feature descriptor for image retrieval in bio-medical images

Srivastava, Varun; Purwar, Ravindra Kumar

doi:10.14201/adcaij2018717789

Cited by 7 publications

(4 citation statements)

References 31 publications

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“…Existing approaches cannot efficiently use the saliency zones of images, and human perception of images cannot be well stored [13,14]. In addition, typical feature extraction techniques overlook the spatial structure of images, which is an essential quality for image retrieval [15][16][17]. Another popular feature is texture.…”

Section: Figure 1 Sample Images For Cbirmentioning

confidence: 99%

“…This pattern is utilized to describe color images. For the effective utilization of the LBP data from different channels, two multichannel decoded LBP [2,15] methods are suggested in this paper: multichannel adder-based LBP (maLBP) and multichannel decoder-based LBP (mdLBP). A sum of (c+1) element and (2c) of resulted channels are created as of (c) number of input-based channels of (c≥2) utilizing a multichannel adder and decoder, respectively.…”

Section: Multichannel Decoded Local Binary Pattern (Mdlbp)mentioning

confidence: 99%

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Content-Based Image Recovery System with the Aid of Median Binary Design Pattern

Mehbodniya¹,

Webber²,

Devi³

et al. 2023

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Content-based image retrieval is a technique for locating images in vast, unlabeled image collections (CBIR). However, users are not happy with the traditional methods of information retrieval. Additionally, the number of consumer-accessible pictures and online production and distribution channels are expanding. Consequently, permanent and widespread digital image processing occurs across numerous industries. As a result, acquiring quick access to these big image databases and extracting identical images from sizable groups of photographs from a specific image (Query) create significant problems that call for efficient solutions. Calculations related to similarity and feature representation are crucial to a CBIR system's effectiveness. Color, shape, texture, and gradient are some essential features that can be utilized to portray an image. Local Binary Pattern (LBP) is a modest and successful texture controller that marks the pixels of an image by controlling the part of every pixel and deciphering the outcome as a binary value. The Local Binary Pattern (LBP) approach is acquainted with grey-level images to characterize color images as the pattern's dimensionality is enhanced. The current study proposes the 'Median Binary Pattern', which incorporates the multichannel decoded Local Binary Pattern (mdLBP) utilized to portray color images. For consolidating LBPs from more than one channel to make the descriptor noise-robust, two structures, specifically adder and decoder-based structures, and a noise-robust binary pattern called the 'Median Binary Pattern'. Compared with existing approaches, the proposed method achieved Average Recovery precision (ARP) and Average Recovery rate (ARR) of 68.1 and 33.55, respectively, with Noise Robust Binary Patterns.

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Section: Figure 1 Sample Images For Cbirmentioning

confidence: 99%

Section: Multichannel Decoded Local Binary Pattern (Mdlbp)mentioning

confidence: 99%

Content-Based Image Recovery System with the Aid of Median Binary Design Pattern

Mehbodniya¹,

Webber²,

Devi³

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Also, they have focused on minimizing long-term service delays and computation costs under the resource along with deadline constraints. To intend for this problem, they have applied the reinforcement learning approach and have presented a Double Deep Q-Learning (DDQL)-based scheduling algorithm using the target network and experience replay techniques [56][57][58][59][60][61][62]. Finally, they have validated that their proposed algorithm outperforms other states of the arts.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED CHAPTER: An Energy-Aware Dynamic Resource Management Technique Using Deep Q-Learning Algorithm and Joint VM and Container Consolidation Approach for Green Computing in Cloud Data Centers

Gholipour

Shoeibi

Arianyan

2021

Advances in Intelligent Systems and Computing

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These days by a high increase in the amount of computation and big data gathering and analysis, everybody needs more resources. Buying more computational and storage resources are so expensive. However, cloud computing solved this problem by providing a "pay as you go" plans therefor, users will only pay for resources that they used. However, using this technology has its challenges. One of them is resource management, which is focusing on the methodologies of dedicating resources to the users with the minimum of waste. In this paper, we propose a novel energy-aware resource management technique, using the concepts of both joint VM and container consolidation approach and deep Q-Learning algorithm for green computing in cloud data centers in order to minimize the waste of resources, migration rate, and energy.

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“…To check the performance of the system, various similarity thresholds have been used to measure the similarity between the queried ROI and extracted ROIs and those extracted ROIs with similarity more than threshold value have been used for classification purpose. Other image retrieval techniques that use texture-based features instead of shape-based features are summarized in [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Classification of CT Scan Images of Lungs Using Deep Convolutional Neural Network with External Shape-Based Features

Srivastava

Purwar

2019

J Digit Imaging

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In this paper, a simplified yet efficient architecture of a deep convolutional neural network is presented for lung image classification. The images used for classification are computed tomography (CT) scan images obtained from two scientifically used databases available publicly. Six external shape-based features, viz. solidity, circularity, discrete Fourier transform of radial length (RL) function, histogram of oriented gradient (HOG), moment, and histogram of active contour image, have also been identified and embedded into the proposed convolutional neural network. The performance is measured in terms of average recall and average precision values and compared with six similar methods for biomedical image classification. The average precision obtained for the proposed system is found to be 95.26% and the average recall value is found to be 69.56% in average for the two databases.

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An extension of local mesh peak valley edge based feature descriptor for image retrieval in bio-medical images

Cited by 7 publications

References 31 publications

Content-Based Image Recovery System with the Aid of Median Binary Design Pattern

Content-Based Image Recovery System with the Aid of Median Binary Design Pattern

RETRACTED CHAPTER: An Energy-Aware Dynamic Resource Management Technique Using Deep Q-Learning Algorithm and Joint VM and Container Consolidation Approach for Green Computing in Cloud Data Centers

Classification of CT Scan Images of Lungs Using Deep Convolutional Neural Network with External Shape-Based Features

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