Colour and orientation of pixel based video retrieval using IHBM similarity measure

Ram, R. Saravana; Prakash, S. Arun; BalaAnand, M.; Sivaparthipan, C. B.

doi:10.1007/s11042-019-07805-9

Cited by 13 publications

(3 citation statements)

References 21 publications

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“…The 5G cloud computing environment [29] and adversarial networks generation [30] address next-generation demands for parallel computing. Low energy consumption [31] with image color recognition [32] are reconfigured for the network connectivity actions.…”

Section: Literature Surveymentioning

confidence: 99%

Industrial Centric Node Localization and Pollution Prediction Using Hybrid Swarm Techniques

Ram¹,

Kumar²,

Krishnamoorthy³

et al. 2022

Computer Systems Science and Engineering

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Major fields such as military applications, medical fields, weather forecasting, and environmental applications use wireless sensor networks for major computing processes. Sensors play a vital role in emerging technologies of the 20th century. Localization of sensors in needed locations is a very serious problem. The environment is home to every living being in the world. The growth of industries after the industrial revolution increased pollution across the environment. Owing to recent uncontrolled growth and development, sensors to measure pollution levels across industries and surroundings are needed. An interesting and challenging task is choosing the place to fit the sensors. Many meta-heuristic techniques have been introduced in node localization. Swarm intelligent algorithms have proven their efficiency in many studies on localization problems. In this article, we introduce an industrial-centric approach to solve the problem of node localization in the sensor network. First, our work aims at selecting industrial areas in the sensed location. We use random forest regression methodology to select the polluted area. Then, the elephant herding algorithm is used in sensor node localization. These two algorithms are combined to produce the best standard result in localizing the sensor nodes. To check the proposed performance, experiments are conducted with data from the KDD Cup 2018, which contain the name of 35 stations with concentrations of air pollutants such as PM, SO 2 , CO, NO 2 , and O 3 . These data are normalized and tested with algorithms. The results are comparatively analyzed with other swarm intelligence algorithms such as the elephant herding algorithm, particle swarm optimization, and machine learning algorithms such as decision tree regression and multi-layer perceptron. Results can indicate our proposed algorithm can suggest more meaningful locations for localizing the sensors in the topology. Our proposed method achieves a lower root mean square value with 0.06 to 0.08 for localizing with Stations 1 to 5.

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Section: Literature Surveymentioning

confidence: 99%

Industrial Centric Node Localization and Pollution Prediction Using Hybrid Swarm Techniques

Ram¹,

Kumar²,

Krishnamoorthy³

et al. 2022

Computer Systems Science and Engineering

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“…It helps to manage field programmable gate array in to reconfigure network for high performance. In [20] proposes content based retrieval of video using histogram of oriented gradient (HOG) for high accurate video selection. Image is given as input and histograms of image helps to retrieve the video.…”

Section: Literature Reviewmentioning

confidence: 99%

Air Pollution Prediction Using Dual Graph Convolution LSTM Technique

Ram¹,

Venkatachalam²,

Masud³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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In current scenario, Wireless Sensor Networks (WSNs) has been applied on variety of applications such as targets tracking, natural resources investigation, monitoring on unapproachable place and so on. Through the sensor nodes, the information for the applications is gathered and transferred. The physical coordination of these sensor nodes is determined, and it is called as localization. The WSN localization methods are studied widely for recent research with the study of small proportion of the sensor node called anchor nodes and their positions are determined through the GPS devices. Sometimes sensor nodes can be a IoT device in the network. With despite this, among the various applications, air pollution and air quality monitoring having many issues on how to place the sensor network in a wide area to monitor the air pollutants level such as carbon dioxide (CO2), nitrogen dioxides (NO2), particulate matter (PM), sulphur dioxide (SO2), ammonia (NH3) and other toxic gases involved in human and industrial activities. The responsibility of the WSN in air quality monitoring is to be positioning the sensor nodes in the large area with low cost and also gather the real time data and produce the monitoring system as an accurate one. In this proposed work, deep learning-based approach called dual graph convolution and LSTM (Long Short-Term Memory) network based (air quality index) AQI predictions were performed. This uses the infrared based technology to measure the CO2, temperature and humidity, Geo statistic method and low power wireless networking. Accuracy of the proposed system is maximum of 95% which is higher than existing techniques.

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“…For instance, a deep CNN based framework for surgery videos retrieval is introduced in [14]. Similarly, there are a lot of matching techniques based on various types of algorithms including deep learning and statistical methods to achieve CBVR task effectively [1,[15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Event-Oriented 3D Convolutional Features Selection and Hash Codes Generation Using PCA for Video Retrieval

et al. 2020

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The extensive video surveillance networks gather an enormous amount of data exponentially on a daily basis and its management is a challenging task, requiring efficient and effective techniques for searching, indexing, and retrieval. The employed mainstream techniques are focusing on general category videos, where the important events in surveillance require fine-grained events retrieval. In this paper, we introduce an event-oriented feature selection mechanism by utilizing the intermediate convolutional layer of a pre-trained 3D-CNN model, that is selected after deep investigation of its weights and response to a particular event. The extracted exclusive features represent an event semantically and effectively eliminate those neurons which do not respond to that event. Furthermore, the event-oriented convolutional features are of very high-dimensions, requiring additional storage, and take more time in feature comparison for retrieval. Therefore, we generate compact binary codes from these features using principle component analysis (PCA) algorithm. This makes our system more efficient to retrieve videos from large scale database. We evaluated our approach on the challenging events of UCF101 and HMDB51 datasets for original features and generated compact codes, achieving faster execution time, and better precision and recall scores. INDEX TERMS Deep learning, feature selection, video retrieval, video analytics, hash codes, surveillance event analysis.

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Colour and orientation of pixel based video retrieval using IHBM similarity measure

Cited by 13 publications

References 21 publications

Industrial Centric Node Localization and Pollution Prediction Using Hybrid Swarm Techniques

Industrial Centric Node Localization and Pollution Prediction Using Hybrid Swarm Techniques

Air Pollution Prediction Using Dual Graph Convolution LSTM Technique

Event-Oriented 3D Convolutional Features Selection and Hash Codes Generation Using PCA for Video Retrieval

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