6533b854fe1ef96bd12adf9e
RESEARCH PRODUCT
A new image segmentation approach using community detection algorithms
Youssef MourchidHocine CherifiMohammed El Hassounisubject
Computer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONScale-space segmentationImage processing02 engineering and technology[INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE][INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]03 medical and health sciences0302 clinical medicine[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]Image textureMinimum spanning tree-based segmentation020204 information systems0202 electrical engineering electronic engineering information engineering[INFO]Computer Science [cs]Computer visionSegmentationComputingMilieux_MISCELLANEOUSbusiness.industrySegmentation-based object categorization[INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM]Pattern recognitionImage segmentationRegion growingArtificial intelligencebusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingAlgorithm030217 neurology & neurosurgerydescription
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 is invariant to non-structural change on image, which mean that the image segmentation is also invariant to rotation. Finally we evaluate the three proposed algorithms for Berkeley database images, and we show that our results can outperform other segmentation methods in terms of accuracy and can achieve much better segmentation results.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2015-12-14 | 2015 15th International Conference on Intelligent Systems Design and Applications (ISDA) |