Beyond chi 2 Difference: Learning Optimal Metric for Boundary Detection

• Published in: IEEE signal processing letters Vol. 22; no. 1; pp. 40 - 44
• Main Authors: He, Fei, Wang, Shengjin
• Format: Journal Article
• Language: English
• Published: 01.01.2015
• Subjects: Algorithms; Borders; Boundaries; Image detection; Learning; Neural networks; Optimization; Semantics
• ISSN: 1070-9908; 1558-2361
• DOI: 10.1109/LSP.2014.2346232

This letter focuses on solving the challenging problem of detecting natural image boundaries. A boundary usually refers to the border between two regions with different semantic meanings. Therefore, a measurement of dissimilarity between image regions plays a pivotal role in boundary detection of natural images. To improve the performance of boundary detection, a Learning-based Boundary Metric (LBM) is proposed to replace chi 2 difference adopted by the classical algorithm mPb. Compared with chi 2 difference, LBM is composed of a single layer neural network and an RBF kernel, and is fine-tuned by supervised learning rather than human-crafted. It is more effective in describing the dissimilarity between natural image regions while tolerating large variance of image data. After substituting chi 2 difference with LBM, the F-measure metric of mPb on the BSDS500 benchmark is increased from 0.69 to 0.71. Moreover, when image features are computed on a single scale, the proposed LBM algorithm still achieves competitive results compared with mPb, which makes use of multi-scale image features.