scholarly journals Is visual image segmentation a bottom-up or an interactive process?

1997 ◽  
Vol 59 (8) ◽  
pp. 1280-1296 ◽  
Author(s):  
Shaun P. Vecera ◽  
Martha J. Farah
Keyword(s):  
Image Segmentation ◽  
Visual Image ◽  
Bottom Up ◽  
Interactive Process

An Image Retrieval Algorithm Based on Region Segmentation

2014 ◽  
Vol 596 ◽  
pp. 337-341 ◽  
Author(s):  
Xiao Mei Xiong ◽  
Yong Lang Liu
Keyword(s):  
Image Segmentation ◽  
Image Retrieval ◽  
Visual Image ◽  
Retrieval Algorithm ◽  
Manual Annotation ◽  
Region Segmentation ◽  
Long Time ◽  
And Performance ◽  
Time Image ◽  
Rich Content

Visual image, as a kind of rich content and performance of multimedia information, has been tremendously popular for a long time. Image retrieval technology is complicated than text retrieval, due to text-based image retrieval is often need manual annotation, so very laborious and individual subjective factors are there. In order to solve these problems, this paper puts forward a kind of image retrieval algorithm based on improved region segmentation. First, use of image segmentation technology, dividing the image into several regions, then to match each region and the being tested image, and obtained retrieval results in the end. It can be seen through experiment, the user only needs to submit a retrieval image, so it can greatly reduce the user's retrieval burden, and improve the efficiency of retrieval.

Bottom-Up Shift and Reasoning for Referring Image Segmentation

2021 ◽  
Author(s):  
Sibei Yang ◽  
Meng Xia ◽  
Guanbin Li ◽  
Hong-Yu Zhou ◽  
Yizhou Yu
Keyword(s):  
Image Segmentation ◽  
Bottom Up

The Initial Retrieval Based on Image Segmentation

2014 ◽  
Vol 919-921 ◽  
pp. 2131-2134
Author(s):  
Qing Hua Zhan
Keyword(s):  
Image Segmentation ◽  
Image Retrieval ◽  
Image Matching ◽  
Visual Image ◽  
Segmentation Method ◽  
Image Library ◽  
Long Time ◽  
And Performance ◽  
Rich Content ◽  
Initial Retrieval

Visual image, as a kind of rich content and performance of multimedia information, has been tremendously popular for a long time. Using text-based Image Retrieval TBIR (Text-based Image Retrieval, TBIR) during retrieval will provide keywords and description of the image text matching, operation simple and quick. The defects of TBIR also, however, there are the following: (1) image library image all the need for manual annotation, time-consuming and laborious with subjective factors; (2) image semantics is rich, simple key words cannot fully express its meaning and accurate. Image Retrieval Based on regional RBIR (Region-based Image Retrieval, RBIR) first of all, by using image segmentation method, divides an image into several different regions. At last image matching is converted to match between the regions. We just need to user submits a retrieval image, greatly reducing the user's retrieval burden.

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