A novel pixel range calculation technique for texture classification

Ranganath, Abadhan; Senapati, Manas Ranjan; Sahu, Pradip Kumar

doi:10.1007/s11042-022-12186-7

Cited by 4 publications

(2 citation statements)

References 40 publications

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“…Particularly, if the volume of images to be handled is large viz thousands of images in art galleries, large numbers of satellite imagery, etc. [27][28][29][30]. The supervised texture segmentation techniques are difficult to provide the prior knowledge manually, and this has motivated the present researchers to look for fully automated schemes.…”

Section: Methodsmentioning

confidence: 99%

Multi-Resolution Feature Embedded Level Set Model for Crosshatched Texture Segmentation

Prabhakar¹,

K.M.²

2023

Int. j. electr. comput. eng. syst. (Online)

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In image processing applications, texture is the most important element utilized by human visual systems for distinguishing dissimilar objects in a scene. In this research article, a variational model based on the level set is implemented for crosshatched texture segmentation. In this study, the proposed model’s performance is validated on the Brodatz texture dataset. The cross-hatched texture segmentation in the lower resolution texture images is difficult, due to the computational and memory requirements. The aforementioned issue has been resolved by implementing a variational model based on the level set that enables efficient segmentation in both low and high-resolution images with automatic selection of the filter size. In the proposed model, the multi-resolution feature obtained from the frequency domain filters enhances the dissimilarity between the regions of crosshatched textures that have low-intensity variations. Then, the resultant images are integrated with a level set-based active contour model that addresses the segmentation of crosshatched texture images. The noise added during the segmentation process is eliminated by morphological processing. The experiments conducted on the Brodatz texture dataset demonstrated the effectiveness of the proposed model, and the obtained results are validated in terms of Intersection over the Union (IoU) index, accuracy, precision, f1-score and recall. The extensive experimental investigation shows that the proposed model effectively segments the region of interest in close correspondence with the original image. The proposed segmentation model with a multi-support vector machine has achieved a classification accuracy of 99.82%, which is superior to the comparative model (modified convolutional neural network with whale optimization algorithm). The proposed model almost showed a 0.11% improvement in classification accuracy related to the existing model.

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Section: Methodsmentioning

confidence: 99%

Multi-Resolution Feature Embedded Level Set Model for Crosshatched Texture Segmentation

Prabhakar¹,

K.M.²

2023

Int. j. electr. comput. eng. syst. (Online)

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“…The feature extraction ability of LSTM and its variants is the opposite of LSTM and its variants have the opposite feature extraction capability to TextCNN. Therefore, the key to this problem lies in how to perform text representation and design network models to improve the accuracy of news text classification [13][14].…”

Section: Key Issues In News Text Classificationmentioning

confidence: 99%

Machine Learning Based News Text Classification

2020

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With the rapid development of the Internet era, the explosive growth of news data volume and the lack of effective management are gradually becoming serious problems, and it is increasingly difficult for readers to obtain valuable information quickly. How to quickly search for valuable information from the large amount of news text information is a meaningful task in text classification. Existing research methods still have some problems, such as directly combining the headline with the text, thus neglecting the importance of the headline, and the single model of classification, which leads to low classification results. For this reason, the main objective of this paper is to investigate the classification of news texts based on machine learning. This paper examines the current state of deep learning-based text classification and, in combination with the characteristics of news texts, chooses a machine learning-based text classification method to further explore and study news texts. Through the response time and core algorithm accuracy test of this system, the system better reflects the excellent performance of the system and meets the actual performance requirements of the system. The proposed news system can not only process system requests quickly, but also has excellent accuracy rate, which can better assist users to filter information and improve the user's experience of reading news.

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