showing 8 related works from this author
A General Framework for Complex Network-Based Image Segmentation
2019
International audience; With the recent advances in complex networks theory, graph-based techniques for image segmentation has attracted great attention recently. In order to segment the image into meaningful connected components, this paper proposes an image segmentation general framework using complex networks based community detection algorithms. If we consider regions as communities, using community detection algorithms directly can lead to an over-segmented image. To address this problem, we start by splitting the image into small regions using an initial segmentation. The obtained regions are used for building the complex network. To produce meaningful connected components and detect …
Movie Script Similarity Using Multilayer Network Portrait Divergence
2020
International audience; This paper addresses the question of movie similarity through multilayer graph similarity measures. Recent work has shown how to construct multilayer networks using movie scripts, and how they capture different aspects of the stories. Based on this modeling, we propose to rely on the multilayer structure and compute different similarities, so we may compare movies, not from their visual content, summary, or actors, but actually from their own storyboard. We propose to do so using “portrait divergence”, which has been recently introduced to compute graph distances from summarizing graph characteristics. We illustrate our approach on the series of six Star Wars movies.
Assessing movie similarity using a multilayer network model
2021
International audience; This work investigates the movie similarity issue. A multilayer network model capturing various aspects of the story is built from movie scripts. Based on this representation, movies are compared not from summary or actors but using their storyboard. We rely on the "Portrait divergence" to quantify distances between graph characteristics. We illustrate the effectiveness of this approach in comparing movie series.
Image Segmentation by Deep Community Detection Approach
2017
International audience; To address the problem of segmenting an image into homogeneous communities this paper proposes an efficient algorithm to detect deep communities in the image by maximizing at each stage a new centrality measure, called the local Fiedler vector centrality (LFVC). This measure is associated with the sensitivity of algebraic connectivity to node removals. We show that a greedy node removal strategy, based on iterative maximization of LFVC, has bounded performance loss relative to the optimal, but intractable, combinatorial batch removal strategy. A remarkable feature of this method is the ability to segments the image automatically into homogeneous regions by maximizing…
Multilayer Network Model of Movie Script
2018
Network models have been increasingly used in the past years to support summarization and analysis of narratives, such as famous TV series, books and news. Inspired by social network analysis, most of these models focus on the characters at play. The network model well captures all characters interactions, giving a broad picture of the narration’s content. A few works went beyond by introducing additional semantic elements, always captured in a single layer network. In contrast, we introduce in this work a multilayer network model to capture more elements of the narration of a movie from its script: people, locations, and other semantic elements. This model enables new measures and insights…
An Image Segmentation Algorithm based on Community Detection
2016
International audience; With the recent advances in complex networks, image segmentation becomes one of the most appropriate application areas. In this context, we propose in this paper a new perspective of image segmentation by applying two efficient community detection algorithms. By considering regions as communities, these methods can give an over-segmented image that has many small regions. So, the proposed algorithms are improved to automatically merge those neighboring regions agglomerative to achieve the highest modularity/stability. To produce sizable regions and detect homogeneous communities, we use the combination of a feature based on the Histogram of Oriented Gradients of the …
A new image segmentation approach using community detection algorithms
2015
Image segmentation has an important role in many image processing applications. Several methods exist for segmenting an image. However, this technique is still a relatively open topic for which various research works are regularly presented. With the recent developments on complex networks theory, image segmentation techniques based on graphs has considerably improved. In this paper, we present a new perspective of image segmentation, by applying three of the most efficient community detection algorithms, Louvain, infomap and stability optimization based on the louvain algorithm, and we extract communities in which the highest modularity feature is achieved. After we show that this measure …
Multilayer Network Model of Movie Script
2018
Network models have been increasingly used in the past years to support summarization and analysis of narratives, such as famous TV series, books and news. Inspired by social network analysis, most of these models focus on the characters at play. The network model well captures all characters interactions, giving a broad picture of the narration's content. A few works went beyond by introducing additional semantic elements, always captured in a single layer network. In contrast, we introduce in this work a multilayer network model to capture more elements of the narration of a movie from its script: people, locations, and other semantic elements. This model enables new measures and insights…