Towards semantic segmentation of orthophoto images using graph-based community identification
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We present an unsupervised framework that automatically detects objects of interest in images by formulating the general problem of semantic segmentation as community detection problem in graphs. The framework broadly follows a four-step procedure. First, we perform an over-segmentation of the original image using the well-known statistical region merging algorithm which presents the advantage of not requiring any quantization or colour space transformations. Second, we compute the feature descriptors of the resulting segmented regions. For encoding colour and other textural information, each region is described by an hybrid descriptor based on colour histograms and covariance matrix descriptor. Third, from the set of descriptors we construct different weighted graphs using various graph construction algorithms. Finally, the resulting graphs are then divided into groups or communities using a community detection algorithm based on spectral modularity maximization. This algorithm makes use of the eigenspectrum of matrices such as the graph Laplacian matrix and the modularity matrix which are more likely to reveal the community structure of the graph. Experiments conducted on large orthophotos depicting several zones in the region of Belfort city situated on the north-eastern of France provide promising results. The proposed framework can be used by semi-automatic approaches to handle the challenging problems of scene parsing.
KeywordsSemantic segmentation Aerial images Feature descriptors Graph construction methods Spectral clustering Community detection
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Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
- 2.Socher R, Lin CC, Ng AY, Manning CD (2011) Parsing natural scenes and natural language with recursive neural networks. In: International conference on machine learningGoogle Scholar
- 3.Sharma P, Suji J (2016) A review on image segmentation with its clustering techniques. Int J Signal Process Image Process Pattern Recognit 9(5):209–218Google Scholar
- 5.Sousa C, Rezende S, Batista G (2013) Influence of graph construction on semi-supervised learning. In: European conference on machine learning, pp 160–175Google Scholar
- 14.Hu H (2015) Graph based models for unsupervised high dimensional data clustering and network analysis. Ph.D. thesis, University of CaliforniaGoogle Scholar
- 16.Garima, Gulati H, Singh PK (2015) Clustering techniques in data mining: a comparison. In: 2015 2nd international conference on computing for sustainable global development (INDIACom), pp 410–415Google Scholar
- 18.Guo C, Zheng S, Xie Y, Hao W (2012) A survey on spectral clustering. In: World Automation Congress 2012, Puerto Vallarta, Mexico, pp 53–56Google Scholar
- 25.Tuzel O, Porikli F, Meer P (2006) A fast descriptor for detection and classification. In: European conference on computer vision, pp 589–600Google Scholar
- 27.Takala V, Ahonen T, Pietikainen M (2005) Block-based methods for image retrieval using local binary patterns. In: Image analysis, SCIA, vol LNCS, p 3540Google Scholar
- 31.Wolf L, Hassner T, Taigman Y (2008) Descriptor based methods in the wild. In: Faces in real-life images workshop in ECCVGoogle Scholar