Superpixels with Content-Awareness via a Two-Stage Generation Framework

被引:0
|
作者
Li, Cheng [1 ]
Liao, Nannan [2 ]
Huang, Zhe [3 ]
Bian, He [1 ]
Zhang, Zhe [1 ]
Ren, Long [1 ]
机构
[1] Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China
[2] Xidian Univ, Inst Intelligent Control & Image Engn, Xian 710071, Peoples R China
[3] Wuhan Second Ship Design & Res Inst, Wuhan 430025, Peoples R China
来源
SYMMETRY-BASEL | 2024年 / 16卷 / 08期
关键词
superpixels; content awareness; centroid relocation; online average clustering;
D O I
10.3390/sym16081011
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The superpixel usually serves as a region-level feature in various image processing tasks, and is known for segmentation accuracy, spatial compactness and running efficiency. However, since these properties are intrinsically incompatible, there is still a compromise within the overall performance of existing superpixel algorithms. In this work, the property constraint in superpixels is relaxed by in-depth understanding of the image content, and a novel two-stage superpixel generation framework is proposed to produce content-aware superpixels. In the global processing stage, a diffusion-based online average clustering framework is introduced to efficiently aggregate image pixels into multiple superpixel candidates according to color and spatial information. During this process, a centroid relocation strategy is established to dynamically guide the region updating. According to the area feature in manifold space, several superpixel centroids are then split or merged to optimize the regional representation of image content. Subsequently, local updating is adopted on pixels in those superpixel regions to further improve the performance. As a result, the dynamic centroid relocating strategy offers online averaging clustering the property of content awareness through coarse-to-fine label updating. Extensive experiments verify that the produced superpixels achieve desirable and comprehensive performance on boundary adherence, visual satisfactory and time consumption. The quantitative results are on par with existing state-of-the-art algorithms in terms with several common property metrics.
引用
收藏
页数:19
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