Color image segmentation on region growing and multi-scale clustering

2011 ◽  
Author(s):  
Zong-pu Jia ◽  
Wei-xing Wang ◽  
Jun-ding Sun ◽  
Tai-wen Wei
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Region Growing ◽  
Color Image Segmentation ◽  
Multi Scale

scholarly journals Objectness Supervised Merging Algorithm for Color Image Segmentation

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Haifeng Sima ◽  
Aizhong Mi ◽  
Zhiheng Wang ◽  
Youfeng Zou
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Region Growing ◽  
Color Image Segmentation ◽  
Semantic Features ◽  
Merging Procedure ◽  
Proposed Model ◽  
Merging Process ◽  
High Level ◽  
Color Texture

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.

scholarly journals Wavelet adaptive quantization based color image segmentation

2020 ◽  
Author(s):  
Anand Swaminathan ◽  
K.Venkata Subramaniyan ◽  
Tiruppathirajan G. ◽  
Rajkumar J
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Region Growing ◽  
Color Image Segmentation ◽  
Modern World ◽  
New Approach ◽  
Processing Step ◽  
Segmentation Algorithms ◽  
Color Similarity ◽  
Homogeneous Regions

Image segmentation is an important pre-processing step towards higher level tasks such as object recognition, computer vision or image compression. Most of the existing segmentation algorithms deal with grayscale images only. But in the modern world, color images are extensively used in many situations. A new approach for color image segmentation is presented in this paper. There are many ways to deal with image segmentation problem and in these techniques; a particular class of algorithms traces their origin from region-based methods. These algorithms group homogeneous pixels, which are connected to primitive regions. They are easy to implement and are promising. Therefore, here one of the most efficient region-based segmentation algorithms is explained. The color image is quantized adaptively, using a wavelet transform. Then the region growing process is adopted. As preprocess, before actual region merging, small regions are eliminated by merging them with neighbor regions depending upon color similarity. After this, homogeneous regions are merged to get segmented output.

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