Title: Assessing the effectiveness of image recognition tools in metadata identification through semantic and label-based analysis

Authors: Akara Thammastitkul

Addresses: Department of Information Studies, Faculty of Humanities and Social Sciences, Burapha University, Saen Suk, Chonburi, Thailand

Abstract: This study evaluates the performance of four image recognition tools (Amazon Rekognition, Clarifai, Imagga and Google Cloud Vision API) for automatic image metadata. The experiment was conducted on various image categories, including human, animal, plant and flower, view and landscape, vegetable and fruit, food, vehicle, tourist landmark, art and culture and old book cover and posters. Semantic and label-based analysis was used to evaluate the performance of each tool. Results indicate that each tool performed differently across categories, demonstrating the importance of selecting the appropriate tool for specific tasks. Clarifai was found to perform best for human, animal and food image tagging, while Amazon Rekognition was best for vegetable-fruit and vehicle images. Imagga performed best for plant and flower, art and culture and old book cover and posters image recognition, while Google Cloud Vision API performed best for view and landscape and tourist landmark recognition.

Keywords: image recognition tool; automatic image metadata; evaluation; performance assessment; image category; semantic; label-based.

DOI: 10.1504/IJMSO.2023.137174

International Journal of Metadata, Semantics and Ontologies, 2023 Vol.16 No.3, pp.227 - 237

Received: 09 Mar 2023
Accepted: 08 Dec 2023
Published online: 04 Mar 2024 *

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