Hierarchical Feature Selection for Efficient Image Segmentation

Detailed Description

The opencv hfs module contains an efficient algorithm to segment an image. This module is implemented based on the paper Hierarchical Feature Selection for Efficient Image Segmentation, ECCV 2016. The original project was developed by Yun Liu(https://github.com/yun-liu/hfs).

Introduction to Hierarchical Feature Selection

This algorithm is executed in 3 stages:

In the first stage, the algorithm uses SLIC (simple linear iterative clustering) algorithm to obtain the superpixel of the input image.

In the second stage, the algorithm view each superpixel as a node in the graph. It will calculate a feature vector for each edge of the graph. It then calculates a weight for each edge based on the feature vector and trained SVM parameters. After obtaining weight for each edge, it will exploit EGB (Efficient Graph-based Image Segmentation) algorithm to merge some nodes in the graph thus obtaining a coarser segmentation After these operations, a post process will be executed to merge regions that are smaller then a specific number of pixels into their nearby region.

In the third stage, the algorithm exploits the similar mechanism to further merge the small regions obtained in the second stage into even coarser segmentation.

After these three stages, we can obtain the final segmentation of the image. For further details about the algorithm, please refer to the original paper: Hierarchical Feature Selection for Efficient Image Segmentation, ECCV 2016

Classes
class	cv::hfs::HfsSegment