Texture retrieval using periodically extended and adaptive curvelets

The energy and resources saving has become a major task of petrochemical enterprises, it is necessary to construct the energy saving diagnostic system for understand the real time operation information of petrochemical plant and provide theoretical basis for taking energy saving measures. The energy saving diagnosis process of petrochemical plant based on twin Curvelet support vector machine optimized by hybrid glowworm swarm algorithm is designed. The Curvelet kernel function is constructed based on curvelet transform to establish theory model of twin curvelet support vector machine. In order to improve the prediction precision of the twin curvelet support vector machine, the hybrid glowworm swarm optimization algorithm is constructed based on simulated annealing simulation to optimize the parameters of the twin Curvelet support vector machine. Finally, a petrochemical plant is used as research object to carry out diagnosis simulation analysis, and results showed that the proposed prediction model can effectively improve diagnostic effectiveness of the energy saving effect of petrochemical plant.

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“…In this paper, we use the texture feature as the second major feature to effectively measure the similarity between two images. In an image, texture is generally used as one of the major matching features because it represents roughness of an image pixel distribution [21][22][23][24]. Conventional methods for extracting textures in the field of pattern recognition include many forms such as local binary pattern (LBP), wavelet transform, and so on.…”

Section: Feature Extractionmentioning

confidence: 99%

Effective Image Quality Inspection Based on Color and Texture Features

Jang*,

Lee*

2020

IJRTE

0

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As people’s interest in software increases, the number and types of newly developed applications increase day by day. Therefore, there are attempts to automatically check whether the newly developed interface of these programs works correctly through image processing instead of manually. In this paper, we describe a strategy to robustly detect execution error screens of applications through comparative analysis of key features from various kinds of image scenes. In the approach described in this study, we extract the main multiple features representing the image. Then, by comparing the difference of the extracted multiple features, it is effectively determined whether the image is the same normal image as the reference image or an error image similar to the target image but different from each other. Experimental results verify that the described algorithm accurately detects normal and faulty images by comparing the main multiple features from various types of images. For performance evaluation, we quantitatively compared the performance of the introduced dissimilar region extraction strategy with those of other conventional methods. The existing methods of extracting similarity between images based on one feature or histogram comparison mostly lack information to sufficiently express the entire image. Therefore, when similar areas between two images are extracted, many errors occurred due to incorrect matching. However, the proposed multiple feature-based method obtains richer information by extracting multiple features from the input image. In addition, because the matching is done based on this information, the accuracy is relatively high. The approach introduced in this study is expected to be effectively used in many practical applications related to image processing such as image retrieval, trademark comparison, video security, application quality inspection, etc.

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“…defined by w , the polar coordinates in frequency domain are r and  . is mother function of Curvelet, and then every Curvelet under j scale can be obtained through rotation and translation of mother function The division amount of angle is defined by A , and the rotation angle is defined by[24] …”

mentioning

confidence: 99%

Energy saving diagnosis model of petrochemical plant based on intelligent curvelet support vector machine

Zhao

¹

,

Qin

²

,

Gao

³

et al. 2021

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The energy and resources saving has become a major task of petrochemical enterprises, it is necessary to construct the energy saving diagnostic system for understand the real time operation information of petrochemical plant and provide theoretical basis for taking energy saving measures. The energy saving diagnosis process of petrochemical plant based on twin Curvelet support vector machine optimized by hybrid glowworm swarm algorithm is designed. The Curvelet kernel function is constructed based on curvelet transform to establish theory model of twin curvelet support vector machine. In order to improve the prediction precision of the twin curvelet support vector machine, the hybrid glowworm swarm optimization algorithm is constructed based on simulated annealing simulation to optimize the parameters of the twin Curvelet support vector machine. Finally, a petrochemical plant is used as research object to carry out diagnosis simulation analysis, and results showed that the proposed prediction model can effectively improve diagnostic effectiveness of the energy saving effect of petrochemical plant.

show abstract

Texture retrieval using periodically extended and adaptive curvelets

Cited by 9 publications

References 36 publications

Energy Saving Diagnosis Model of Petrochemical Plant Based on Intelligent Curvelet Support Vector Machine

Energy Saving Diagnosis Model of Petrochemical Plant Based on Intelligent Curvelet Support Vector Machine

Effective Image Quality Inspection Based on Color and Texture Features

Energy saving diagnosis model of petrochemical plant based on intelligent curvelet support vector machine

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