A Multiple-Objects Recognition Method Based on Region Similarity Measures: Application to Roof Extraction from Orthophotoplans

In this paper, an efficient method for automatic and accurate detection of multiple objects from images using a region similarity measure is presented. This method involves the construction of two knowledge databases: The first one contains several distinctive textures of objects to be extracted. The second one is composed with textures representing background. Both databases are provided by some examples (training set) of images from which one wants to recognize objects. The proposed procedure starts by an initialization step during which the studied image is segmented into homogeneous regions. In order to separate the objects of interest from the image background, an evaluation of the similarity between the regions of the segmented image and those of the constructed knowledge databases is then performed. The proposed approach presents several advantages in terms of applicability, suitability and simplicity. Experimental results obtained from the method applied to extract building roofs from orthophotoplans prove its robustness and performance over popular methods like K Nearest Neighbours (KNN) and Support Vector Machine (SVM).

@article{Idrissi2015,
  title     = {A Multiple-Objects Recognition Method Based on Region Similarity Measures: Application to Roof Extraction from Orthophotoplans},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {6},
  number    = {11},
  year      = {2015},
  publisher = {The Science and Information Organization},
  author    = {Abdellatif El Idrissi and Youssef El Merabet and Yassine Ruichek and Raja Touahni and Abderrahmane Sbihi and Cyril Meurie and Ahmed Moussa},
  doi       = {10.14569/IJACSA.2015.061139},
  url       = {https://doi.org/10.14569/IJACSA.2015.061139}
}