Evolutionary computation-based self-supervised learning for image processing: a big data-driven approach to feature extraction and fusion for multispectral object detection

The image object recognition and detection technology are widely used in many scenarios. In recent years, big data has become increasingly abundant, and big data-driven artificial intelligence models have attracted more and more attention. Evolutionary computation has also provided a powerful drivin...

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Veröffentlicht in:	Journal of big data Jg. 11; H. 1; S. 130 - 20
Hauptverfasser:	Shen, Xiaoyang, Li, Haibin, Shankar, Achyut, Viriyasitavat, Wattana, Chamola, Vinay
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 01.12.2024 Springer Nature B.V SpringerOpen
Schlagworte:	Artificial intelligence Big Data Communications Engineering Computation Computational Science and Engineering Computer Science Data integration Data Mining and Knowledge Discovery Data processing Database Management Deep learning Evolutionary algorithms Evolutionary computation Extraction Feature extraction Feature recognition Image enhancement Image processing Imagery Information Storage and Retrieval Infrared imagery Learning Machine learning Mathematical Applications in Computer Science Networks Object detection Object recognition Optimization Parameter identification Self-supervised learning Target detection Big data Image processing Evolutionary computation Self-supervised learning Object detection
ISSN:	2196-1115, 2196-1115
Online-Zugang:	Volltext
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