Graph-based zero-shot learning for classifying natural and computer-generated image

• Published in: Multimedia tools and applications Vol. 83; no. 25; pp. 65987 - 66011
• Main Authors: Prasad, K. Vara, Abdul, Ashu, Srikanth, B., Paleti, Lakshmikanth, Kumar, K. Kranthi, Pachala, Sunitha
• Format: Journal Article
• Language: English
• Published: New York Springer US 20.01.2024; Springer Nature B.V
• Subjects: Classification; Computer Communication Networks; Computer Science; Computer vision; Data Structures and Information Theory; Feature extraction; Image classification; Multimedia Information Systems; Special Purpose and Application-Based Systems; Zero-shot learning; Feature extraction; Zero-shot learning; Unseen images; Computer-generated image; Preprocessing; Buffalo optimization
• ISSN: 1573-7721; 1380-7501; 1573-7721
• DOI: 10.1007/s11042-023-18026-6

The zero-shot image classification is a stimulating problem that attains the human recognition level depending upon the tiny quantity of trained images. Image classification was an essential phenomenon in the computer vision process. Therefore, the major problem was solving the classification process, and generally, the processing of entire data for the extraction process was complicated. To solve this problem, a proposed novel technique was Buffalo-based Graph Neural Zero-short Learning (BbGZSL) that aimed to classify the image types as natural and computer-generated. In the first stage, the denoised process was performed to eliminate the data noise and convert the colour image into a grey scale image. Then, a feature extraction process was performed to extract the required features based on the buffalo fitness features of the proposed model. Furthermore, the extracted features were stored using the learning memory. Finally, perform the unseen image testing and matching process for classifying the image. In addition, the proposed BbGZSL mechanism was implemented in the Python tool with several performance assessments. The proposed model gained 97.06% accuracy, f-score and Recall, as well as 97.07% precision for the tested unseen image dataset.