Improvements on the Superpixel Hierarchy Algorithm with Applications to Image Segmentation and Saliency Detection

EasyChair Preprint no. 4309

Abstract

Superpixel techniques aim to divide an image into a different predefined number of regions or groups of pixels, to facilitate operations such as segmentation. However, finding the optimal number of regions for each image becomes a difficult task due to the large difference of features observed in images. However, with the help of edge and color information, we can target an ideal number of regions for each image. This work presents two modifications to the known Superpixel hierarchy algorithm. These changes aim to define the number of superpixels automatically through edge information with different orientations and the Hue channel of the HSV color model. The results are presented quantitatively and qualitatively for edge detection and saliency estimation problems. The experiments were conducted on the BSDS500 and ECSSD datasets.

Keyphrases: image segmentation, Mean Shift, superpixel

@Booklet{EasyChair:4309,
  author = {Marcos José Canejo and Carlos Alexandre Barros de Mello},
  title = {Improvements on the Superpixel Hierarchy Algorithm with Applications to Image Segmentation and Saliency Detection},
  howpublished = {EasyChair Preprint no. 4309},

  year = {EasyChair, 2020}}