@inproceedings{1595a635d9754ed5a202751cae7ddd38,
title = "Image Sampling for Machine Vision",
abstract = "Image sampling is one of the basic methods for image compression, which is efficient for image store, transmission, and applications. Existing sampling methods are designed for human-eye perception, which discard unconcerned information to decrease the amount of data considering the visual preference of human. However, these methods cannot adapt to the increasing machine vision tasks since there is a lot of redundant information for machine analysis to ensure the comfort of human eyes. In this paper, we propose an image sampling method for machine vision. We adopt a gray image to retain the main structural information of the image, and construct a concise color feature map based on the dominant channel of pixels to provide color information. Experiments on public datasets including COCO and ImageNet show that our sampling method can adapt to the characteristics of machine vision and greatly reduce the amount of data with little impact on the performance of mainstream computer vision algorithms.",
keywords = "Color feature, Image sampling, Machine vision",
author = "Jiashuai Cui and Fan Li and Liejun Wang",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 2nd CAAI International Conference on Artificial Intelligence, CAAI 2022 ; Conference date: 27-08-2022 Through 28-08-2022",
year = "2022",
doi = "10.1007/978-3-031-20497-5\_19",
language = "英语",
isbn = "9783031204968",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "229--241",
editor = "Lu Fang and Daniel Povey and Guangtao Zhai and Tao Mei and Ruiping Wang",
booktitle = "Artificial Intelligence - Second CAAI International Conference, CICAI 2022, Revised Selected Papers",
}