Segmentation of large images based on super-pixels and community detection in graphs
| Autor: | Oscar Cuadros Linares, Francisco A. Rodrigues, João do Espirito Santo Batista Neto, Glenda Michele Botelho |
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| Rok vydání: | 2017 |
| Předmět: |
Pixel
business.industry Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Graph partition Graph theory Pattern recognition 02 engineering and technology Image segmentation 01 natural sciences Object detection Rendering (computer graphics) 0103 physical sciences Signal Processing 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Segmentation Computer Vision and Pattern Recognition Artificial intelligence Electrical and Electronic Engineering Medical diagnosis 010306 general physics business APRENDIZADO COMPUTACIONAL Software |
| Zdroj: | Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual) Universidade de São Paulo (USP) instacron:USP |
| Popis: | Image segmentation has many applications which range from machine learning to medical diagnosis. In this study, the authors propose a framework for the segmentation of images based on super-pixels and algorithms for community identification in graphs. The super-pixel pre-segmentation step reduces the number of nodes in the graph, rendering the method the ability to process large images. Moreover, community detection algorithms provide more accurate segmentation than traditional approaches based on spectral graph partition. The authors also compared their method with two algorithms: (i) the graph-based approach by Felzenszwalb and Huttenlocher and (ii) the contour-based method by Arbelaez. Results have shown that their method provides more precise segmentation and is faster than both of them. |
| Databáze: | OpenAIRE |
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