ICTACT Journal on Image and Video Processing

N. Ananthi ¹, Thiyam Ibungomacha Singh ², Nihar Ranjan Behera ³, R. K. Gnanamurthy ⁴

Affiliations
1 Department of Information Technology, Easwari Engineering College, IN
2 Department of Computer Science and Engineering, Manipur Institute of Technology, IN
3 Geneva Business Center, Swiss School of Business and Management, CH
4 Department of Electronics and Communication Engineering, VSB College of Engineering Technical Campus, IN

Abstract

This research presents a novel approach that combines the C4.5 algorithm with Adversarial Learning-based Adaptive Data Augmentation (ADA) for Color and Multispectral Processing, leading to a significant enhancement in Image Analysis. The C4.5 algorithm, known for its decision tree construction, is integrated with ADA, which employs adversarial learning principles to generate diverse and realistic training samples. This integration enables the augmentation of both color and multispectral images, effectively boosting the robustness and accuracy of image analysis tasks. The proposed method showcases improved performance in various applications such as object recognition, classification, and scene understanding. Experimental results demonstrate the superiority of the proposed approach compared to traditional methods, substantiating its potential for advancing image analysis techniques.

Keywords

C4.5 algorithm, Adversarial Learning, Adaptive Data Augmentation (ADA), Color Processing, Multispectral Processing, Image Analysis

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Kaavya Kanagaraj ¹, Shilpa Abhang ², Julakanti Sampath Kumar ³, R. K. Gnanamurthy ⁴, V. Balaji ⁵

Affiliations
1 Department of Computational Intelligence, SRM Institute of Science and Engineering, Kattankulathur Campus, IN
2 Department of MCA, Jyoti Nivas College, IN
3 Department of Electronics and Communication Engineering, ACE Engineering College, IN
4 Department of Electronics and Communication Engineering, VSB College of Engineering Technical Campus, IN
5 Department of Electrical and Electronics Engineering, MAI-NEFHI College of Engineering and Technology Asmara, ER

Abstract

The explosive growth of video data poses a significant challenge in retrieving relevant content swiftly. Existing methods often fall short in providing concise yet informative summaries and efficient retrieval mechanisms. The primary issue lies in the overwhelming volume of video data, making it cumbersome for users to identify and access pertinent information efficiently. Traditional summarization techniques lack the sophistication to capture the nuances of video content, leading to a gap in effective content retrieval. Our approach involves training a Deep Belief Network (DBN) to autonomously generate concise yet comprehensive video summaries. Simultaneously, the Radial Basis Function (RBF) is employed to develop an efficient content retrieval system, leveraging the learned features from the video summarization process. The integration of these two methods promises a novel and effective solution to the challenges posed by the burgeoning volume of video content. Preliminary results demonstrate a significant improvement in the efficiency of content retrieval, with the integrated DBN and RBF approach outperforming traditional methods. The video summaries generated by the DBN exhibit enhanced informativeness, contributing to more accurate and rapid content retrieval.

Keywords

Video Summarization, DBN, Content Retrieval, RBF, Multimedia Content

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