Paper Info
Title
IFT-SLIC: A General Framework for Superpixel Generation Based on Simple Linear Iterative Clustering and Image Foresting Transform
Abstract
Image representation based on super pixels has become indispensable for improving efficiency in Computer Vision systems. Object recognition, segmentation, depth estimation, and body model estimation are some important problems where super pixels can be applied. However, super pixels can influence the efficacy of the system in positive or negative manner, depending on how well they respect the object boundaries in the image. In this paper, we improve super pixel generation by extending a popular algorithm--Simple Linear Iterative Clustering (SLIC)--to consider minimum path costs between pixel and cluster centers rather than their direct distances. This creates a new Image Foresting Transform (IFT) operator that naturally defines super pixels as regions of strongly connected pixels by choice of the most suitable path-cost function for a given application. Non-smooth connectivity functions are also explored in our IFT-SLIC approach leading to improved performance. Experimental results indicate better super pixel extraction using the proposed approach as compared to that of SLIC.
Year
DOI
Venue
2015
10.1109/SIBGRAPI.2015.20
Brazilian Symposium on Computer Graphics and Image Processing
Keywords
Field
DocType
Simple Linear Iterative Clustering,Image Foresting Transform,Superpixel,unsupervised segmentation
Computer vision,Pattern recognition,Segmentation,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Pixel,Cluster analysis,Strongly connected component,Mathematics,Pixel connectivity,Cognitive neuroscience of visual object recognition
Conference
ISSN
Citations 
PageRank 
1530-1834
5
0.41
References 
Authors
18
4
Name
Order
Citations
PageRank
Eduardo Barreto-Alexandre150.75
Ananda S. Chowdhury220733.29
Alexandre Xavier Falcão316710.22
Paulo A. V. Vechiatto Miranda431326.26