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Advances in Multimedia
Volume 2018, Article ID 4724078, 10 pages
https://doi.org/10.1155/2018/4724078
Research Article

Selective Feature Fusion Based Adaptive Image Segmentation Algorithm

Department of Computer Science and Technology, Key Laboratory of Embedded System and Service Computing, Tongji University, Shanghai, China

Correspondence should be addressed to Zhihua Wei; nc.ude.ijgnot@iew_auhihz

Received 6 June 2018; Revised 20 August 2018; Accepted 28 August 2018; Published 9 September 2018

Academic Editor: Marco Roccetti

Copyright © 2018 Qianwen Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Image segmentation is an essential task in computer vision and pattern recognition. There are two key challenges for image segmentation. One is to find the most discriminative image feature set to get high-quality segments. The other is to achieve good performance among various images. In this paper, we firstly propose a selective feature fusion algorithm to choose the best feature set by evaluating the results of presegmentation. Specifically, the proposed method fuses selected features and applies the fused features to region growing segmentation algorithm. To get better segments on different images, we further develop an algorithm to change threshold adaptively for each image by measuring the size of the region. The adaptive threshold can achieve better performance on each image than fixed threshold. Experimental results demonstrate that our method improves the performance of traditional region growing by selective feature fusion and adaptive threshold. Moreover, our proposed algorithm obtains promising results and outperforms some popular approaches.