Understanding Marker-Based Normalization for FLIM Networks

Leonardo Joao, Leonardo Joao, Matheus Cerqueira, Barbara Benato, Alexandre Falcao

2024

Abstract

Successful methods for object detection in multiple image domains are based on convolutional networks. However, such approaches require large annotated image sets for network training. One can build object detectors by exploring a recent methodology, Feature Learning from Image Markers (FLIM), that considerably reduces human effort in data annotation. In FLIM, the encoder’s filters are estimated among image patches extracted from scribbles drawn by the user on discriminative regions of a few representative images. The filters are meant to create feature maps in which the object is activated or deactivated. This task depends on a z-score normalization using the scribbles’ statistics, named marker-based normalization (MBN). An adaptive decoder (point-wise convolution with activation) finds its parameters for each image and outputs a saliency map for object detection. This encoder-decoder network is trained without backpropagation. This work investigates the effect of MBN on the network’s results. We detach the scribble sets for filter estimation and MBN, introduce a bot that draws scribbles with distinct ratios of object-and-background samples, and evaluate the impact of five different ratios on three datasets through six quantitative metrics and feature projection analysis. The experiments suggest that scribble detachment and MBN with object oversampling are beneficial.

Paper Citation

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP
TI - Understanding Marker-Based Normalization for FLIM Networks
SN - 978-989-758-679-8
AU - Joao L.
AU - Cerqueira M.
AU - Benato B.
AU - Falcao A.
PY - 2024
SP - 612
EP - 623
DO - 10.5220/0012385900003660
PB - SciTePress

in Harvard Style

Joao L., Cerqueira M., Benato B. and Falcao A. (2024). Understanding Marker-Based Normalization for FLIM Networks. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 612-623. DOI: 10.5220/0012385900003660

in Bibtex Style

@conference{visapp24,
author={Leonardo Joao and Matheus Cerqueira and Barbara Benato and Alexandre Falcao},
title={Understanding Marker-Based Normalization for FLIM Networks},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP},
year={2024},
pages={612-623},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012385900003660},
isbn={978-989-758-679-8},
}