Spatial Similarity Measure of Visual Phrases for Image Retrieval

2014 ◽  
pp. 275-282 ◽  
Author(s):  
Jiansong Chen ◽  
Bailan Feng ◽  
Bo Xu
Keyword(s):  
Image Retrieval ◽  
Similarity Measure ◽  
Spatial Similarity ◽  
Visual Phrases

scholarly journals Image Retrieval with Scale Invariant Visual Phrases

2013 ◽  
Vol E96.D (5) ◽  
pp. 1063-1067
Author(s):  
Deying FENG ◽  
Jie YANG ◽  
Cheng YANG ◽  
Congxin LIU
Keyword(s):  
Image Retrieval ◽  
Scale Invariant ◽  
Visual Phrases

Similarity Measure for Image Retrieval Based on Hausdorff Distance

2014 ◽  
Vol 635-637 ◽  
pp. 1039-1044 ◽  
Author(s):  
He Qun Qiang ◽  
Chun Hua Qian ◽  
Sheng Rong Gong
Keyword(s):  
Image Retrieval ◽  
Pattern Matching ◽  
Similarity Measure ◽  
Hausdorff Measure ◽  
Hausdorff Distance ◽  
Experimental Result ◽  
Feature Vectors ◽  
Image Query ◽  
Feature Values ◽  
Object Similarity

In general, it is difficult to segment accurately image regions or boundary contours and represent them by feature vectors for shape-based image query. Therefore, the object similarity is often computed by their boundaries. Hausdorff distance is nonlinear for computing distance, it can be used to measure the similarity between two patterns of points of edge images. Classical Hausdorff measure need to express image as a feature matrix firstly, then calculate feature values or feature vectors, so it is time-consuming. Otherwise, it is difficult for part pattern matching when shadow and noise existed. In this paper, an algorithm that use Hausdorff distance on the image boundaries to measure similarity is proposed. Experimental result has showed that the proposed algorithm is robust.

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