Research Article
On-Device Image Labelling Photo Management System Using Flutter and ML Kit
@INPROCEEDINGS{10.1007/978-3-030-99188-3_13, author={Tan Chi Wee and Ken Ng Chen Kee}, title={On-Device Image Labelling Photo Management System Using Flutter and ML Kit}, proceedings={Intelligent Technologies for Interactive Entertainment. 13th EAI International Conference, INTETAIN 2021, Virtual Event, December 3-4, 2021, Proceedings}, proceedings_a={INTETAIN}, year={2022}, month={3}, keywords={Computer vision Object recognition Artificial intelligence Flutter Machine learning}, doi={10.1007/978-3-030-99188-3_13} }- Tan Chi Wee
Ken Ng Chen Kee
Year: 2022
On-Device Image Labelling Photo Management System Using Flutter and ML Kit
INTETAIN
Springer
DOI: 10.1007/978-3-030-99188-3_13
Abstract
Automatic image annotation is the process by which the system automatically assigns relevant labels (metadata) to a digital image. This type of computer vision technique is mainly used in image retrieval systems to organize all the data and seek the interest of images from databases. This technique is also considered as a type of multi-class image classification. Regarding the past related work that had been done by the researchers, annotating digital images have also been used for the Academic Health Care Environment to solve the difficulty of business and graphic arts commercial-off-the-shelf (COTS) software in multi-context authoring and interactive teaching environments. As many pre-trained machine models have been created for the past few years, the requirement for existing models still needs a large set of data to be imported, and the usage of CPU hours is tremendously expensive. Google cloud API can outperform existing models in terms of computational complexity in obtaining image labels. The ML Kit firebase associated with Google Cloud Vision API is idealistically suited in this application, which can be useful in returning a set of labels that comes with a score that indicates confidence the ML model has in its relevance. With all of these labels, assembling all images on related labels is no longer a troublesome issue, and it can be quickly searched by querying on the back-end part.
