Objectness Supervised Merging Algorithm for Color Image Segmentation

Haifeng Sima,Aizhong Mi,Zhiheng Wang,Youfeng Zou

doi:10.1155/2016/3180357

Abstract

Ideal color image segmentation needs both low-level cues and high-level semantic features. This paper proposes a two-hierarchy segmentation model based on merging homogeneous superpixels. First, a region growing strategy is designed for producing homogenous and compact superpixels in different partitions. Total variation smoothing features are adopted in the growing procedure for locating real boundaries. Before merging, we define a combined color-texture histogram feature for superpixels description and, meanwhile, a novel objectness feature is proposed to supervise the region merging procedure for reliable segmentation. Both color-texture histograms and objectness are computed to measure regional similarities between region pairs, and the mixed standard deviation of the union features is exploited to make stop criteria for merging process. Experimental results on the popular benchmark dataset demonstrate the better segmentation performance of the proposed model compared to other well-known segmentation algorithms.

Highlights

Color image segmentation is an important task in image analysis and understanding
In order to tackle those issues remaining in existing segmentation methods, we developed a novel color image segmentation method based on merging superpixels supervised by regional objectness and underlying characteristics in this paper
Inspired by the objectness computing method proposed by Jiang et al [11], we propose an improved objectnenss computing method based on sparse sampling of slide windows for all pixels

Summary

Introduction

Color image segmentation is an important task in image analysis and understanding. It is widely used in many image applications as a critical step. Researchers aim to find high-level summary tags or labels to represent all the underlying features With all these elements and textons, the segmentation can employ affinity computing to make decision of pixels labels of semantic objects and regions. They integrated basic visual features to detect homogeneous regions for image segmentation Even though these low-level features have shown favorable results, they are unbefitting in many complex scenes, such as texture, luminance, and overlapping. The main contributions of this paper are summarised as follows: (1) we develop efficacious superpixels extraction method to detect homogeneous regions with hybrid features, (2) we propose a combined histogram descriptor of color and texture feature for identifying semantic regions, and (3) we design a fast and less sampling objectness computing model and integrate it with low-level features for reasonable merging.

Objectness Supervised Superpixels Merging Segmentation

Experimental Results

Conclusion