ICTACT Journal on Image and Video Processing

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Feature Extraction Using AT-ConvLSTM Based Cultural Algorithm for Image Understanding


Affiliations
1 Department of Electronics and Communication, Shri Jagdishprasad Jhabarmal Tibrewala University, India
2 Department of Computer Science and Engineering, Shri Jagdishprasad Jhabarmal Tibrewala University, India
3 Department of Electrical and Electronics Engineering, Shri Jagdishprasad Jhabarmal Tibrewala University, India
     

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This research presents a novel approach for feature extraction in image understanding, utilizing an AT-ConvLSTM-based Cultural Algorithm. The Proposed CA-AT-ConvLSTM leverages the power of deep learning through AT-ConvLSTM architecture while optimizing the feature extraction process using Cultural Algorithms. This synergistic approach enhances the efficiency and accuracy of image understanding tasks, making it suitable for a wide range of applications, from computer vision to pattern recognition. The experimental results demonstrate the superiority of the proposed technique over traditional methods, highlighting its potential in advancing the field of image analysis.

Keywords

Feature Extraction, AT-ConvLSTM, Cultural Algorithm, Image Understanding, Deep learning
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  • Feature Extraction Using AT-ConvLSTM Based Cultural Algorithm for Image Understanding

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Authors

Shweta Nishit Jain
Department of Electronics and Communication, Shri Jagdishprasad Jhabarmal Tibrewala University, India
Priya Pise
Department of Computer Science and Engineering, Shri Jagdishprasad Jhabarmal Tibrewala University, India
Akhilesh Mishra
Department of Electrical and Electronics Engineering, Shri Jagdishprasad Jhabarmal Tibrewala University, India

Abstract


This research presents a novel approach for feature extraction in image understanding, utilizing an AT-ConvLSTM-based Cultural Algorithm. The Proposed CA-AT-ConvLSTM leverages the power of deep learning through AT-ConvLSTM architecture while optimizing the feature extraction process using Cultural Algorithms. This synergistic approach enhances the efficiency and accuracy of image understanding tasks, making it suitable for a wide range of applications, from computer vision to pattern recognition. The experimental results demonstrate the superiority of the proposed technique over traditional methods, highlighting its potential in advancing the field of image analysis.

Keywords


Feature Extraction, AT-ConvLSTM, Cultural Algorithm, Image Understanding, Deep learning

References