Generator of a Toy Dataset of Multi-Polygon Monochrome Images for Rapidly Testing and Prototyping Semantic Image Segmentation Networks

2019
Vadim Romanuke

In the paper, the problem of building semantic image segmentation networks in a more efficient way is considered. Building a network capable of successfully segmenting real-world images does not require a real semantic image segmentation task. At this stage, called prototyping, a toy dataset can be used. Such a dataset can be artificial and thus may not need augmentation for training. Besides, its entries are images of much smaller size, which allows training and testing the network a way faster. Objects to be segmented are one or few convex polygons in one image. Thus, a toy dataset generator is created whose complexity is regulated by the number of edges in a polygon, the maximal number of polygons in one image, the set of scale factors, and the set of probabilities determining how many polygons in a current image are generated. The dataset capacity and image size are concurrently adjustable, although they are much less influential.

Keywords
Dataset complexity; Multi-polygon object; Semantic image segmentation; Segmentation network architecture; Toy dataset; Two-class segmentation
DOI
10.2478/ecce-2019-0008
Hyperlink
https://doi.org/10.2478/ecce-2019-0008

Romanuke, V. Generator of a Toy Dataset of Multi-Polygon Monochrome Images for Rapidly Testing and Prototyping Semantic Image Segmentation Networks. Electrical, Control and Communication Engineering, 2019, Vol. 15, No. 2, pp. 54-61. ISSN 2255-9140. e-ISSN 2255-9159. Available from: doi:10.2478/ecce-2019-0008

Publication language
English (en)