Weakly Supervised Multi-Graph Learning for Robust Image Reranking

2014 ◽  
Vol 16 (3) ◽  
pp. 785-795 ◽  
Author(s):  
Cheng Deng ◽  
Rongrong Ji ◽  
Dacheng Tao ◽  
Xinbo Gao ◽  
Xuelong Li
Keyword(s):  
Graph Learning ◽  
Robust Image ◽  
Weakly Supervised ◽  
Image Reranking

Contextual modeling on auxiliary points for robust image reranking

2019 ◽  
Vol 13 (5) ◽  
pp. 1010-1022
Author(s):  
Ying Li ◽  
Xiangwei Kong ◽  
Haiyan Fu ◽  
Qi Tian
Keyword(s):  
Contextual Modeling ◽  
Robust Image ◽  
Image Reranking

Visual Reranking through Weakly Supervised Multi-graph Learning

2013 ◽  
Author(s):  
Cheng Deng ◽  
Rongrong Ji ◽  
Wei Liu ◽  
Dacheng Tao ◽  
Xinbo Gao
Keyword(s):  
Graph Learning ◽  
Weakly Supervised

Image Reranking Using Multimodal Sparse Coding

2019 ◽  
Vol 7 (1) ◽  
pp. 277-282
Author(s):  
Mohammadi Aiman ◽  
Ruksar Fatima
Keyword(s):  
Sparse Coding ◽  
Image Reranking

Multiresolution Bio-Medical Image Segmentation using Fuzzy C-Means Clustering

2019 ◽  
Vol 8 (4) ◽  
pp. 9548-9551
Keyword(s):  
Image Segmentation ◽  
Medical Image Segmentation ◽  
Experimental Result ◽  
Segmentation Method ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Robust Image ◽  
Multiple Clusters ◽  
Single Pixel ◽  
Popular Image

Fuzzy c-means clustering is a popular image segmentation technique, in which a single pixel belongs to multiple clusters, with varying degree of membership. The main drawback of this method is it sensitive to noise. This method can be improved by incorporating multiresolution stationary wavelet analysis. In this paper we develop a robust image segmentation method using Fuzzy c-means clustering and wavelet transform. The experimental result shows that the proposed method is more accurate than the Fuzzy c-means clustering.

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