Convolutional Mean: A Simple Convolutional Neural Network for Illuminant Estimation

Gong, Han (2019) Convolutional Mean: A Simple Convolutional Neural Network for Illuminant Estimation. In: British Machine Vision Conference (BMVC) 2019. BMVA Press.

[img]
Preview
PDF (BMVC_2019) - Published Version
Download (8MB) | Preview

Abstract

We present Convolutional Mean (CM) – a simple and fast convolutional neural network for illuminant estimation. Our proposed method only requires a small neural network model (1.1K parameters) and a 48 × 32 thumbnail input image. Our unoptimized Python implementation takes 1 ms/image, which is arguably 3-3750× faster than the current leading solutions with similar accuracy. Using two public datasets, we show that our proposed light-weight method offers accuracy comparable to the current leading methods’ (which consist of thousands/millions of parameters) across several measures.

Item Type: Book Section
Uncontrolled Keywords: illuminant estimation,convolution,computer vision and pattern recognition ,/dk/atira/pure/subjectarea/asjc/1700/1707
Faculty \ School: Faculty of Science > School of Computing Sciences
Depositing User: LivePure Connector
Date Deposited: 12 Sep 2019 23:52
Last Modified: 02 Aug 2020 23:57
URI: https://ueaeprints.uea.ac.uk/id/eprint/72118
DOI:

Actions (login required)

View Item View Item