TITLE = {{Maximal Independent Vertex Set applied to Graph Pooling}}, AUTHOR = {Stanovic, Stevan and Ga{"u}z{`e}re, Benoit and Brun, Luc}, BOOKTITLE = {{Structural and Syntactic Pattern Recognition (SSPR)}}, ADDRESS = {Montr{'e}al, Canada}, YEAR = {2022}, MONTH = Aug, KEYWORDS = {Graph Neural Networks ; Graph Pooling ; Graph Classification ; Maximal Independant Vertex Set ; Graph Neural Networks}, url= {HAL:=https://hal.archives-ouvertes.fr/hal-03739114, pdf:= https://hal.archives-ouvertes.fr/hal-03739114/file/main.pdf, ArXiv:=https://arxiv.org/abs/2208.01648}, theme="pattern", abstract={Convolutional neural networks (CNN) have enabled major advances in image classification through convolution and pooling. In particular, image pooling transforms a connected discrete grid into a reduced grid with the same connectivity and allows reduction functions to take into account all the pixels of an image. However, a pooling satisfying such properties does not exist for graphs. Indeed, some methods are based on a vertex selection step which induces an important loss of information. Other methods learn a fuzzy clustering of vertex sets which induces almost complete reduced graphs. We propose to overcome both problems using a new pooling method, named MIVSPool. This method is based on a selection of vertices called surviving vertices using a Maximal Independent Vertex Set (MIVS) and an assignment of the remaining vertices to the survivors. Consequently, our method does not discard any vertex information nor artificially increase the density of the graph. Experimental results show an increase in accuracy for graph classification on various standard datasets.} }@inproceedings{CI-GOFFE-2011, author = {Romain {Goffe} and Luc {Brun} and Guillaume {Damiand}}, title = {Tiled top down pyramids and segmentation of large histological images}, booktitle = {In 8th IAPR - TC-15 Workshop on Graph-based Representations in Pattern Recognition (GBR'11)}, publisher = {Springer}, editor = {Xiaoyi {Jiang} and Miquel {Ferrer} and Andrea {Torsello}}, series = {Lecture Notes in Computer Science}, volume = {6658}, pages = {255-264}, year = {2011}, month = {May}, url = {HAL:= http://hal.archives-ouvertes.fr/hal-00596703, pdf:=http://hal.archives-ouvertes.fr/docs/00/59/67/03/PDF/GoffeAl11-GBR.pdf,poster(pdf):=http://hal.archives-ouvertes.fr/docs/00/59/67/03/ANNEX/GoffeAl11-GBR-poster_1_.pdf}, keywords = {Irregular pyramid; Topological model; Combinatorial map;}, abstract = { Recent microscopic imaging systems such as whole slide scanners provide very large (up to 18GB) high resolution images. Such amounts of memory raise major issues that prevent usual image representation models from being used. Moreover, using such high resolution images, global image features, such as tissues, does not clearly appear at full resolution. Such images contain thus different hierarchical information at different resolutions. This paper presents the model of tiled top-down pyramids which provides a framework to handle such images. This model encodes a hierarchy of partitions of large images defined at different resolutions. We also propose a generic construction scheme of such pyramids whose validity is evaluated on an histological image application. }, theme = {hierarchical}