Open Access

ARTICLE

Automatic Image Annotation Using Adaptive Convolutional Deep Learning Model

R. Jayaraj^1,*, S. Lokesh²

1 Department of Computer Science and Engineering, Hindusthan College of Engineering and Technology, Coimbatore, 641032, Tamilnadu, India
2 Department of Computer Science and Engineering, Hindusthan Institute of Technology, Coimbatore, 641032, Tamilnadu, India

* Corresponding Author: R. Jayaraj. Email:

Intelligent Automation & Soft Computing 2023, 36(1), 481-497. https://doi.org/10.32604/iasc.2023.030495

Received 27 March 2022; Accepted 29 June 2022; Issue published 29 September 2022

Abstract

Every day, websites and personal archives create more and more photos. The size of these archives is immeasurable. The comfort of use of these huge digital image gatherings donates to their admiration. However, not all of these folders deliver relevant indexing information. From the outcomes, it is difficult to discover data that the user can be absorbed in. Therefore, in order to determine the significance of the data, it is important to identify the contents in an informative manner. Image annotation can be one of the greatest problematic domains in multimedia research and computer vision. Hence, in this paper, Adaptive Convolutional Deep Learning Model (ACDLM) is developed for automatic image annotation. Initially, the databases are collected from the open-source system which consists of some labelled images (for training phase) and some unlabeled images {Corel 5 K, MSRC v2}. After that, the images are sent to the pre-processing step such as colour space quantization and texture color class map. The pre-processed images are sent to the segmentation approach for efficient labelling technique using J-image segmentation (JSEG). The final step is an automatic annotation using ACDLM which is a combination of Convolutional Neural Network (CNN) and Honey Badger Algorithm (HBA). Based on the proposed classifier, the unlabeled images are labelled. The proposed methodology is implemented in MATLAB and performance is evaluated by performance metrics such as accuracy, precision, recall and F1_Measure. With the assistance of the proposed methodology, the unlabeled images are labelled.

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Keywords

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